Wachter noted emerging data suggesting that when novices are exposed to highly capable, predictive tools too early, it can actually stunt their cognitive development. We’ve discussed this many times, and I KNOW we’ve discussed these things across the country. If a tool writes the note or summarizes the chart perfectly from day one, how does a resident learn the fundamental “blocking and tackling” of clinical reasoning? Blocking and tackling is a popular phrase originating from American football (for the unaware) that refers to mastering the basic, foundational skills required to succeed. While it describes the physical core of football, it is widely used as a metaphor in corporate, leadership, and operational strategies. We aren’t quite ready to take the cognitive heavy lifting off the plate, even if we are finally ready to retire the Krebs cycle from the mandatory memory banks. You can’t score if the blocking isn’t happening.

The concept that resonated with me most deeply was his breakdown of the “human-in-the-loop” dynamic and why expert use of AI is fundamentally different from novice use. Wachter shared a striking study from Oxford where patients and an AI model interacted. When the AI was given a textbook prompt of the “worst headache of your life” (the classic clinical indicator for a life-threatening subarachnoid hemorrhage), it correctly told the user to go straight to the ER. But when actual patients interacted with the bot, they phrased it as “a really bad headache,” causing the tool to mistakenly recommend rest and Tylenol. For those in medicine, the phraseology seems so obvious, but the context is missed, like in idiom translation. “Take it with a grain of salt” may not make sense in Arabic or Spanish like it does in English.

As an educator, this was a massive lightbulb moment for me. Experts instinctively know which 5 out of 200 disparate data points are the salient ones to feed into a prompt, and they possess the clinical judgment to look at an AI’s output and say, “No, that’s dangerous and stupid.” Novices simply don’t have that interpretive layer yet. If we don’t intentionally train our residents to understand when these tools are trustworthy, how to spot subtle automation bias, and how to remain eternally vigilant when a system has been right 39 times in a row, we are failing them. We have to teach them to be the final, human arbiter in high-stakes environments.

Ultimately, the interview left me feeling challenged. It’s easy to write newsletters, give presentations, and philosophize about how AI will democratize and improve our clinical worlds. But the gritty work happens on the ground; building the guardrails, designing the curricula that teach both raw clinical reasoning and advanced tool utilization, and ensuring our trainees don’t lose their analytical edge.

It also left me feeling a bit bothered that we’re bogged down as clinicians, unable to innovate at times. This year, I aim to spend less time talking about healthcare technology and more time doing the things to build it. The status quo of medical education won’t evolve on its own, and our new class of residents deserves a curriculum that prepares them for the reality of the world they are inheriting.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

This is a guest post from Meghan Coles, our wonderful residency program coordinator. She is a GME pro, and I am (so) grateful that she is such a forward thinker and problem solver. I suggest you share this with your respective coordinator - especially if they need encouragement to try. Have them subscribe for more use cases and tips coming.

I’m not new to my career, but I’m not at the end of it either. Caught somewhere in the middle of getting a social media account when it was only available to those with a .edu email and finding myself in a position where I’m looked to for my experience. As we end the academic year and many of us look towards ‘what’s next’ there are underlying questions of how AI will alter job functions and how to keep up with new technology.

After more than 15 years in higher education, I have come to appreciate the importance of continually strengthening efficiency, adaptability, and leadership skills to navigate an ever-evolving role successfully. Responsibilities and expectations continue to shift, requiring ongoing growth and flexibility. This evolution is especially evident in the coordinator role, which has expanded significantly in both scope and responsibility. What began as a primarily secretarial position focused on filing, scheduling, and manual paperwork evolved in the early 2000s with increased accreditation requirements and the transition to electronic management systems. Today, residency administrators oversee complex operational responsibilities including budgeting, strategic planning, recruitment branding, faculty and resident support, data analytics, and increasingly sophisticated scheduling and program demands.

I was in a retreat recently with a group of coordinators. We all laughed that we are paid as one full-time employee, but that the workload is at the level of at least one and a half people. The same group joked not long after that when recruiting new hires, one of the most common questions is about work-life balance. I feel it. There is a constant challenge of balancing the demands of an ever-growing workload while still being fully present with my family at the end of the day. It’s building a career, limiting burnout, and continually carrying the weight of a never-ending to-do list.

I’m fortunate to be in a position where AI has been embraced, not as a solution, but as a support function, a tool. Last year, I was working on schedules for 8 residents – balancing if/then relationships, finessing individual opportunities, personal requests, and managing complex and conflicting priorities. A lot of how I approach this work comes from the mentors who taught and guided me throughout my career. I learned by putting pen to paper and in many cases, working cell by cell in spreadsheets. I color-coded, reviewed, edited, and asked for help when needed.

Another year and eight more residents, effectively doubling every individual scenario and responsibility. I continue managing didactic plans, coordinating lecturers and guest speakers, and overseeing calendar invitations and scheduling logistics, but now on a larger scale. The role continued to expand; the core work remained the same, but the volume and complexity steadily increased. While the need for additional support was recognized and discussed, it often remained just out of reach due to limitations in finances, time, and space.

Then, my department supported a paid version of an AI platform. We were encouraged to play with it, use it where we felt it could be beneficial. Up until this point, I had only used one AI portal. I used it to make my emails smoother, more polished, more professional, or help to get my point across when the words wouldn’t come. The new platform allowed me to expand my requests into other tools I used daily – spreadsheets, documents, calendaring, creating forms and managing responses.

My world exploded.

Not only was I able to ask AI to do the task for me, but it was able to take what I asked and create something that I could use, share, and build upon. It’s not perfect. I’ve had to double-check my work and micro-manage my AI tool. I’ve had to use my experience to make appropriate adjustments and understand the make-up of my role to finesse the prompts so that I get the output I need.

At the same time, using AI has increased my productivity and ability leave the office at a reasonable time knowing that I can accomplish what used to take me weeks in just a few hours. Using AI has become a creative outlet. I’ve been able to build something fun and innovative. It has sparked new ideas and more ‘could we do this’ scenarios because I’m not held back by my own technical limitations. I can input the ‘what’ and ‘why’ while I lean on AI to know the ‘how’.

As the role of the coordinator continues to evolve in complexity, embracing AI has helped me adapt, grow, and work more efficiently. It has become a practical tool for managing the constant moving pieces of the job, staying organized, streamlining repetitive tasks - allowing me to be more intentional with the parts of the role that truly require personal connection.

More personally, it has helped me form a healthier balance between my career and life outside of work. By improving my efficiency during the day, I am better able to step away feeling accomplished and confident that meaningful progress was made. I don’t think the goal of AI is to replace the heart of the work. For me, it has created more room for the parts of the job that matter most — being present, helpful, and supportive – and hopefully, to do those parts better.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

A friend and mentor shared the May/June FPM with me last weekend. I opened expecting the usual handful of practice pearls, and instead landed on a piece I wanted to forward to you all. Short post this week. I want to point you at it, and tell you what I’ve been doing lately that pairs nicely with it.

🧭 1: A Piece Worth Your Inbox Time

Dr. Sumana Reddy’s article in FPM, “11 Tips for More Effective AI Clinical Searches”, is a fine piece of work. She walks family docs through PICO-style prompting, asking the model to show its reasoning, requesting GRADE ratings, surfacing counterfactuals, and watching for hallucinations. It is practical, it is grounded in clinical realities (cost, side effects, access), and it is the kind of guidance our learners are hungry for. I appreciated that she names the bias traps too (automation, confirmation, expert bias, sycophancy). If you teach residents/students share it with them today.

🪞 2: Lately I’ve Been Going Meta

Here is the wrinkle I’d add. Lately I’ve been leveraging a lot of meta prompting, that is, asking the model to help me write the prompt before I ask the real question. Something like, “I want to compare two antihypertensives for a complex outpatient. Before you answer, draft me the ideal prompt that includes the patient context, the citations I should require, and the counterfactual checks I should ask for.”

Recently I had Claude prompt for itself a python tool to de-identify pdfs locally (on my computer) so I don’t upload any PHI for a research project. It did a solid job (after some coaching), but still far better than if I prompted myself.

The output of step one becomes the input of step two, and the answers get noticeably sharper. Pair this trick with Dr. Reddy’s eleven tips.

☕ 3: One Small Habit

Try this today. Pick one clinical question you would normally type in one breath. Ask the model to write you a better version of that prompt first. Then run the improved prompt. Notice the difference. That tiny extra step is, in my experience, the cheapest upgrade you can give your clinical AI use this week.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone, have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

I had an email exchange last week with Dr. Xavier Prida, our cardiology fellowship program director and a good friend. It was bringing about whether AI in the exam room is closer to a consultant we curbside, or a third party we have to babysit.

He said some things I have not stopped thinking about. So I wanted to share from the conversation two new pieces of literature. One asks whether the AI can carry the warmth of an encounter. The other asks who gets blamed when the encounter goes wrong.

🤖 1: When the Chatbot Sounds Warmer Than We Do

A new POEM landed in my inbox with a title that, even by POEM standards, is a little provocative: “Chatbots may be able to substitute for humans when responding to medical questions.” (Wiley, Essential Evidence Plus, summarizing Ruben MA, Blanch-Hartigan D, Hall JA. J Gen Intern Med 2025 Dec 8, online ahead of print.)

The setup: 65 real patient questions pulled from Reddit’s r/AskDocs, each answered both by a physician and by ChatGPT. Then 1,454 online raters scored both responses for empathy, quality, trust, liking, and goodness.

The bottom line, in the authors’ own words: “Computers may be learning to be warm and fuzzy.”

A few details worth chewing on:

The chatbot was rated more empathic than the physicians, with P < .001, whether or not the raters were told who wrote the response. Quality, trust, liking, and goodness all followed the same pattern.

When raters were told a response was physician-authored (even when it was actually AI), they rated it as more empathic. So we still get a halo just for being human. We just don’t always earn it on the page.

In the formal coding, chatbot responses contained more validation, reassurance, and nonjudgmental language. They were less rushed and more structured than physician responses.

Two important caveats from the synopsis (Allen Shaughnessy, PharmD, MMedEd, Tufts) that should shape how we read this. The physicians were not responding to their own patients (no relationship, no chart, no chair pulled up to the bedside). And the chatbot has effectively unlimited time at no marginal cost. We have eight minutes and a full waiting room.

So what do I take from it, as an educator?

The structural advantages of the chatbot, time, validation language, calm sentence rhythm, are coachable. Those are things I want to start teaching residents to do explicitly. We can use the chatbot as a mirror, not a competitor. This is not a threat. It is a free coaching resource. I don’t care to admit it, but even texts to my closest family can seem “short” at times because of us being busy. I am not terribly surpised at this - but hope it makes us do better, not worse.

(LOE = 2b, for those keeping score at home.)

🥊 2: Relearning Our Clinical Encounters, with a Third Person(definitely not a person) in the Ring

This brings us to the more uncomfortable piece, an analysis in The BMJ (Mateen and colleagues, BMJ 2025;393:bmj.s874) on what role AI is actually playing in the consultation room. The figure (below) lays out two very different models of AI in clinical care.

On the left, the “industrial loop.” The AI model produces an output. The clinician sits between AI and patient as a “safeguard,” meant to oversee and override. The clinician then carries the decision down to the patient.

On the right, “AI as adviser.” The AI model is one of several advisers, alongside regulators and institutions. The actual relationship, the therapeutic alliance, is between the clinician and the patient as co-reasoners. The AI advises that alliance from the side.

Two different jobs.

Here is where Xavier’s boxing metaphor unfolds. In boxing there is a phrase, “the third man in the ring.” He is the referee. He is in the ring with the two fighters. He cannot win the fight for either of them, and he cannot throw a punch, but he absolutely can stop the fight. He is present, he is governing, and he is not the contest.

In the industrial loop, the clinician is the third man in the ring. We are in there with the AI and the patient, and our job is to override the AI when it is wrong. That sounds noble. It is actually a setup. The BMJ piece calls this the “moral crumple zone,” a term borrowed from autonomous vehicle research. When something goes wrong, the human in the loop absorbs the impact, legally and morally, even when the actual structural decision was made upstream by the model, the vendor, or the institution that deployed it. The crumple zone protects the rest of the car. It does not protect itself.

In the “AI as adviser” model, the third man in the ring is the AI. It is present, it can flag a foul, it can offer a read of the action, but the fight (the actual reasoning, the actual decision, the actual relationship) is between clinician and patient. Regulators and institutions take over the oversight role that was quietly outsourced onto the individual clinician. The clinician stops being a one-person liability.

Why does this matter for medical education? Because the two models train two different physicians.

If you’re an NBA fan - same thing on the court, especially in the playoffs. Last week, this example was of a ref maybe getting too involved. If this was AI - perhaps we wouldn’t be so excited for our learners? or our patients?

The industrial-loop physician learns to be defensive. Every AI output is something to check, override, and document around. The skill being practiced is suspicion. Over time, the muscle that atrophies is independent clinical reasoning, because reasoning has been redefined as adjudicating the AI’s output instead of building your own.

The co-reasoner physician learns to be in dialogue. The skill being practiced is integration: articulating to the patient what the AI suggested, what you think, where you agree, where you don’t, and why. That last bit (”and why”) is the entire ballgame. It is also exactly the kind of bedside articulation we have always wanted residents to develop.

So the question Xavier asked me, “Can we learn as we go?”

I think we can, but only if we are honest about which model our current curriculum is accidentally training.

Teach the override as a reasoning act, not a reflex. When a resident disagrees with an AI suggestion, the evaluable skill is the articulation, not the click. Make them say, out loud and on the record, why.

Name the crumple zone for what it is. Residents need to know that “the AI said so” or “I overrode the AI” both miss the point. The point is the reasoning that sits underneath either action.

Practice the co-reasoner sentence. Something like: “This model is suggesting X. Here is what I am weighing against that. Here is what I would like us to decide together.” That sentence, said out loud in front of a resident/patient, is the point of the BMJ paper.

Boxing is not actually about the referee. It is about the two people in the ring, and whether the contest is fair. AI in medicine is the same. The model is not the story, and really should not be noticed (in my opinion). The clinician and the patient, deciding together, are the story. Our job, as educators, is to keep building physicians who know they are in the ring, not standing outside it watching a machine swing.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone, have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

Let me tell you about a patient I saw recently, we’ll call her Maria. She came in already knowing her diagnosis. She’d typed her symptoms into something online, gotten a confident answer, and spent three days convinced she had something serious. She didn’t. But she’d also missed the part where her real issue, a medication side effect in plain sight. This would have been invisible to any algorithm that didn’t know her full history.

That story kept popping in my head as I explored two new AI health tools making waves: PranaDoc’s AI Doctor and Perplexity Health. Both are sophisticated. Both are well-intentioned.

🩺 PranaDoc’s AI Doctor — The Friendly Front Door

PranaDoc has built something thoughtful. Their AI Doctor is a free, 24/7 symptom checker that can spit out a differential, asks follow-up questions, personalizes its guidance, and helps users decide whether they actually need to see a physician. If they do, a board-certified doctor is available for a $39 video visit, with prescriptions sent directly to the pharmacy.

What I appreciate: it’s honest about its limits. The platform is clear that it’s “not a replacement for professional medical diagnosis,” positioning itself as a smart triage tool rather than a substitute for care. That framing matters to me. In a world where millions delay care due to cost or access, a free tool that helps someone decide whether and how urgently to seek help could be valuable.

What gives me pause: the same thing that always gives me pause. The AI doesn’t know Maria. It doesn’t know her medication list, her health literacy, her anxiety baseline, or the fact that she catastrophizes at 2am because her husband is ill and comes to her senses after some reassurance. Symptom checkers operate on what the patient tells them. We probably agree that patients, are notoriously unreliable at times.

Does this dilute the beauty that is at the heart primary care? There are many similar services. That is for you to decide, but I struggle with it.

🔬 Perplexity Health — When AI Meets Your Actual Data

Perplexity Health is playing a different game entirely, and it’s a fascinating one. This is a platform that connects to your real health data: Apple Health, electronic health records from over 1.7 million care providers, and wearables like Fitbit, Ultrahuman, and Withings (with Oura and Function coming soon). FWIW I do use an Apple Watch and just returned my Whoop. More to come on that if you’re interested, I’m happy to share.

When you ask Perplexity Health a question about your resting heart rate, it can pull your recent activity data, your cardiac history, and your latest bloodwork, all at once. It can generate personalized fitness plans, nutrition guidance, and sleep strategies through AI agents that have actually seen your numbers, not just your complaints.

They’ve also assembled a Health Advisory Board of physicians, researchers, and health tech leaders to pressure-test decisions and clinical safeguards. Health data is encrypted, never used to train AI models, and never sold. Currently rolling out to Pro/Max users in the US.

As a physician, and a data nerd, I find this exciting and complicated. The integration of wearable data with lab values and medical records is something we’ve talked about in medicine for years. Seeing it operationalized is remarkable. But the question I keep asking is: who interprets the interpretation? When a patient sees a trend in their biomarkers and an AI draws a conclusion, the cognitive leap from “here’s what the data shows” to “here’s what you should do” is enormous and that leap has required a physician. This may mean more work, like remote patient monitoring solutions.

💡 What This Means for Us as Educators

Both of these tools are going to walk into our exam rooms before our patients do. It is already happening. Our job as physician educators is to prepare the next generation of clinicians for a world where patients arrive pre-informed, sometimes misinformed, and perhaps attached to what an algorithm told them.

A few things I’d want every resident I train to know:

The AI-informed patient is not the enemy. They engaged with their health. That’s a gift. Our job is to meet them there, validate the curiosity, and place that information into the full context of who they are.

AI is excellent at finding patterns in data. Physicians are trained to know when the pattern doesn’t fit the patient.

Talk to your patients about what they’re using. Ask them. “Did you look this up before you came in?” is an invitation. You might be surprised what they found, and what it tells you about how they experience their own health.

The AI doctor may be in. But so are we.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

Two things landed in my inbox this month from Google — almost on the same day, from two different teams, which was funny. One was a product announcement: a new feature called Notebooks in Gemini that ties their chat assistant and NotebookLM into a single workspace. The other was a white paper: The AI Skills Playbook, a five-step guide to building AI fluency in a workforce.

One is a tool. The other is a framework.

But I read them back-to-back and I ended up thinking they belong together. Tools without skill-building are useless. If you want to grow AI fluency in your learners — or in yourself — this year, you need both. This week, Google happened to give us one of each.

🗂️ Notebooks in Gemini — The Blue Binder, Digitized

Announcement: “Notebooks in Gemini: Your space for ideas, powered by NotebookLM.” Google Blog. April 2026. Read it here → blog.google

Last week, between clinics near a stack of unread medical journals, I pulled up Gemini on my phone to ask a quick question about an outpatient CKD workup. I had to scroll past seven unrelated chats to find the conversation I’d started the day before on TB. By the time I found it, the next patient was ready and I’d lost the thought.

Anyone who leans on an AI assistant in a professional life knows this problem. Chats pile up like unsigned notes— clinical questions, board review, trip planning, lecture outlines. Nothing is organized the way your mind is organized.

Google just launched something small but genuinely useful. Notebooks in Gemini creates a dedicated project space that:

• Lets you pin documents, PDFs, and transcripts to a single workspace

• Accepts custom instructions specific to that project

• Syncs with NotebookLM, so the same source list travels between the chat assistant and the research tool

• Can consolidate past chats into one notebook so you don’t lose the thread

This is akin to Claude’s projects which I’ve been using regularly.

Think of it as the old binder on your shelf — the your didactic notes from years ago as a student, and your favorite algorithms printed out — except it lives inside the assistant you’re already talking to, on the phone in your pocket.

Where I think this lands in a medical life:

A notebook per rotation. The intern on inpatient medicine: one notebook with your team’s DVT prophylaxis references, the hospital’s antibiogram, and a running list of things you learned on rounds. Three months later when you rotate back, you’re not starting from zero.

A notebook per topic you teach. When I sit down to prepare a talk on AI in medical education, I’d rather work in a single space that holds the relevant papers, old decks, and a transcript of the last conversation I had with a colleague. Not scattered across my downloads folder and four email threads.

A notebook per patient panel you keep thinking about. I have a handful of patients who live in the back of my head — complex polypharmacy, HFpEF with comorbid everything, the kind of medicine where the differential is long and the social history is longer. A notebook I can come back to, with guidelines and my own dictation, means that when I’m prepping for Monday’s follow-up the assistant is actually useful instead of generic.

One caveat worth knowing: at launch, Notebooks in Gemini is rolling out on paid tiers (AI Ultra, Pro, Plus) on web, with mobile and free access coming in waves. It’s also not yet available on Workspace or Education accounts — so if your institution gave you a Workspace login that governs your Gmail and calendar, wait a bit.

My takeaways:

• Start with one notebook, not ten. Pick the rotation, topic, or project you’re actively thinking about this month

• Write a one-paragraph custom instruction: who you are, what you want back (evidence-grounded, guideline-cited, flag low-certainty claims)

• The sync with NotebookLM means your sources travel with you — from desk research to a bedside question

• This is not a replacement for reading the chart. It’s a better place to put the scaffolding around your thinking

The quiet productivity tools — the ones that just help you remember what you already know — are the ones that compound.

🎓 The AI Skills Playbook — Translated for Medical Learners

White Paper: “The AI Skills Playbook: Your guide to building an AI-ready workforce.” Google Cloud. 2026. Read it here → services.google.com

Google Cloud’s new playbook is a five-step guide aimed at business leaders. I read it and kept thinking: most of this works — almost word for word — for a program director, a clerkship director, a chief resident, or a learner taking their own training seriously this month. It deserved a translation to medicine…

1. Establish your baseline.

The playbook says survey your team, figure out where they are, pick metrics for success. For a learner, the baseline is personal. Open your browser history from the last week. How are you actually using AI today?

2. Put AI where you already work.

This is the clinically important one. The playbook points out that adoption fails when AI is a separate workflow, and thrives when it lives inside the tools you already use. For residents, this is everything. “Go learn AI” as a separate curriculum will not survive contact with a post-call day. AI skills grow when they solve a friction you’re feeling right now — drafting the discharge summary, composing a letter to a patient, explaining a diagnosis to a family. Meet learners where the work already is, or accept that the initiative will quietly die. Google notes that 75% of practitioners prefer hands-on AI training. That tracks with every learner I’ve supervised.

3. Build a framework for trust.

The playbook’s third step is responsible AI — principles, guidelines, governance. In medicine, we have something to teach industry on this one. Clinical judgment is the discipline of knowing when your instrument is reliable and when it isn’t. AI is another instrument with a confidence interval. My framework for trainees is three questions asked every time they use an AI output:

• Is this a task where a plausible-sounding wrong answer is dangerous?

• Can I verify the key claim in under a minute?

• Would I be comfortable if the patient, my attending, or the state medical board saw exactly how I used this?

Once these questions become habit, they don’t slow anyone down. They’re just the clinical version of trust, but verify.

4. Make learning flexible.

The playbook says adults learn differently — some in long sessions, some in 10-minute bursts. For a medical learner, flexible learning isn’t aspirational; it’s the only kind available. One new prompt pattern a week. One real task you automated and refined by hand before it left your screen. I’ve watched learners try to carve out dedicated “AI time” and fail for months. The ones who actually get fluent do it on the fly — a hundred small times, on real work.

5. Celebrate every milestone.

For Google, this step is about certification and badges. For a medical learner, it’s something more formative: share what you figured out. Present a case at noon conference on how you worked through a differential with an AI tool — and where it misled you. Write a one-pager for your residency on your favorite prompt patterns. Submit a workshop to your specialty’s annual meeting. Teach the intern what you learned last month.

There’s truth in the old teaching that the one who teaches learns twice. In medicine, explaining has always been how knowledge calcifies. AI fluency will be no different — and we’ll need each other to develop it. No one has written the textbook for this yet. We are the first cohort. The path gets made by the ones who walk it and leave notes for the ones coming behind.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

I’m writing this from a small table in New Orleans, after a plate of fried chicken, sweet tea and alligator. I’ve been here for STFM with our AI Taskforce and a bunch of friends from across the country — the kind of friends you only see at conferences but who somehow pick up right where you left off last year.

Thursday night was gumbo, Friday was fried dough and sugar, and Saturday was jambalaya. Every block was jazz — the kind that pours out of an open door.

Our STFM 2026 poster, so feel free to download, share, or steal. Just promise to give us a holler and showcase where you put it and what you do with it!

We brought a project our team has been chewing on for a year: a competency-based readiness framework for introducing AI ambient scribes in family medicine residency. This rubric, five domains, ACGME milestone–anchored, with a clear “meets / does not yet meet” threshold.

I expected a couple passing nods, but many expressed something like “We’ve been waiting for someone to just give us a starting point.”

That, to me, is why we do these academic pursuits. The sets of live music just add to that.

🎷 The Project

Our rubric is anchored to existing ACGME milestones — Clinical Reasoning (PC-4), Documentation (ICS-3), System Awareness (SBP-1), Accountability (PROF-2), and Self-Awareness (PROF-3). The threshold for being ready to use an AI ambient scribe is Level 2 attainment across all five domains.

We piloted it with our 8 PGY-1s. All eight met readiness criteria after six months of training. Faculty advisors did the review of a note they selected

Why did it land? I think because it does three things programs have been thinking to do (timeliness is key):

• It names the actual skills the resident needs before AI offloads any of them — clinical reasoning, system awareness of AI as a vulnerability, accountability for what gets signed.

• It uses the milestones we already have, instead of inventing a parallel evaluation universe nobody has time to maintain.

• It gives faculty a defensible answer to “why does this resident get the scribe and that one doesn’t yet?” The answer is no longer “vibes” or “PGY year.” The answer is the rubric. We intentionally went away from chronology for that reason.

Most program directors I talked to weren’t worried about the technology. They were worried about defending the rollout to their faculty, without their residents. The unfairness was a concern. Many places just do it without a system. I’m happy to share an option for adoption (sounds cool in my head).

🎺 How to Use Our Rubric — and How to Make Your Own

If you want to use our rubric directly: the poster above is yours. Share it with your faculty, drop the five domains into your evaluation platform, and run it on your next class of interns at around the six-month mark. Tell us what works and what doesnt. We genuinely want to know — that’s a potential multi-site validation if anyone is interested!

A lighthearted closing thought. Innovation, like jazz, is mostly improvisation around a clear melody. The rubric is the melody. Your program is the improvisation.

Jazz sometimes has songs in 5/4 time, and our ears find them “pleasantly out of sync” as I heard a speaker say at the conference. That feels like our job in medical education right now.

Start playing. Adjust as you go.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

Last week, a big OpenAI announcement that, depending on how you read it, was either the biggest thing to happen to point-of-care AI this year or the most aggressive customer-acquisition play in healthcare history: ChatGPT for Clinicians — free, NPI-verified, on tap, for every U.S. doc, NP, PA, pharmacist, and psychologist who can prove they are licensed.

Next was in JGME from Carl Preiksaitis — yes, same Preiksaitis from Episode 62 — with a title that reads less like a journal article and more like a small, polite warning shot: “Deciding How Much to Trust AI for Teaching and Assessment.”

One says: here, take the keys.

The other asks: do you actually know how to drive this thing yet?

🩺 The What and the How — ChatGPT for Clinicians, Demystified

The What. ChatGPT for Clinicians is a dedicated workspace built on GPT-5.4, OpenAI’s most capable model for healthcare, given to verified U.S. clinicians for free. Not a discount. Not a 30-day trial. Free, full-fat, with higher rate limits than the consumer free tier and a few features the rest of ChatGPT doesn’t have.

What they claim is inside the workspace:

• Trusted clinical search with real-time, cited answers grounded in peer-reviewed studies, clinical guidelines, and public health sources

• Skills — repeatable workflows you can configure once and reuse forever (think: referral letters, prior authorization templates, discharge instructions) and I believe trying to keep up with OpenEvidence on this too

• Deep research — multi-source literature reviews compiled in minutes with full citations

• Higher GPT-5.4 limits for the long, gnarly, multi-turn conversations that real clinical questions actually look like

• A privacy posture above the consumer tier: conversations in the workspace are not used to train models

The How. You go to chatgpt.com/plans/clinicians, sign in (or create an account), and verify through a third-party identity provider using your NPI number and your active license. That’s it. The verification feels less like applying for a DEA registration and more like setting up a Doximity account — a few minutes, no fax machine, no notarized anything.

What’s in it for you? Let me put it bluntly. If you were going to pay for ChatGPT Plus anyway ($20/month) to do the kinds of things doctors quietly already do — draft a patient letter, summarize a guideline, brainstorm a differential — OpenAI just handed that back to you, plus better features, for typing in a number you already have memorized. You get the better model, the higher limits, the citations, and the privacy posture. The cost of entry is your NPI and a few seconds. But, as my brother in law says “there’s no free lunch”. They’re making a play here with NPI’s.

The cynic in me says: of course it’s free — they want every clinician in America logging in every day so the next round of healthcare contracts writes itself. The pragmatist in me says: that’s true and my residents now have access to a better tool than they had last week. Both can be true.

How Should We Use This With Learners?

First, the detail that matters: eligibility is tied to a verifiable license and an NPI. That means most PGY-1+ residents qualify (they hold state licenses; many institutions issue NPIs early in training). It also means most medical students do not — they don’t yet have NPIs, and even if they do, the license check will fail. Worth thinking about: we now have a tier of AI tooling that’s available to your interns but not to your M3s on the same service.

So how do we actually use this with the learners who do qualify?

⚖️ The Trust Question

Now to the question I get asked the most: is this better than what I’m already using?

There are too many tools. I’m not sure yet. How much should we trust it?

The editorial frames trust as something we have to calibrate, not assign. We don’t trust the workspace because OpenAI told us a benchmark number. We trust it the way we trust a medical student on the team: provisionally, in defined domains, with verification, and with our own judgment as the final authority. Trust is earned task by task. Some tasks (drafting a patient letter? sure) are low-trust-required. Others (deciding whether this hypotensive patient gets fluids or pressors? not yet, not without you in the loop).

Some non-negotiable fine print before you start:

• This is not a medical device. OpenAI says it explicitly: not for diagnosis or treatment. Outputs require clinician oversight, full stop.

• The free clinician workspace is not covered by a BAA. Do not paste PHI. If your institution needs HIPAA coverage for AI, that lives in OpenAI’s healthcare/enterprise track, not the free tier.

We gotta keep asking but how much do you actually trust it, and what are you teaching your learners about that question?

We still need to teach the next generation how to drive — eyes on the road, hands on the wheel, even with the advent of self driving cars.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

This week I want to talk about two articles that landed in my reading pile almost back-to-back and ended up speaking to each other in a way I didn’t expect. One is about physician well-being. The other is about training residents in the age of AI. On the surface — different topics. But at the heart of both is the same uncomfortable question: when we let AI take something off our plate, what exactly are we giving up, and what are we getting back?

I was part of a panel on AI a few weeks ago and this was said (paraphrased) “Every skill I’m AUGMENTED by with AI, something is AMPUTATED”. This left a strong taste in my mouth, that I cannot really do more, like using a GPS, without losing some navigation skill.

The answer, it turns out, is not as simple as “more time = better life” or “less struggle = better learning.” Both papers push back on those easy equations. And both are worth your time.

🧠 1: Comfort vs. Meaning — The Well-Being Equation We Keep Getting Wrong

Article: “Meaning and Comfort in Physician Well-Being: An Integrated Framework” Tung MG, Palamara K, Ripp JA, Saddawi-Konefka D. JAMA. March 17, 2026; Volume 335, Number 11. Read it here → doi:10.1001/jama.2026.0472

I used to think burnout was primarily a logistics problem. Fix the inbox, cut the prior auths, get me home by 5:30, and I’ll be fine. And honestly? For a while, some of that works. But this JAMA Viewpoint from a Harvard/Mount Sinai team pointed out: comfort is not the same as fulfillment, and chasing one without the other is a trap.

The authors use two philosophical concepts — hedonia (the pursuit of comfort, reducing friction) and eudaimonia (the pursuit of meaning, purpose, excellence) — and argue that medicine has been lurching between them like a pendulum, when what we actually need is both at once.

To be honest, I don’t know if I agree. my personal conviction and stance is firmly rooted in the practice of medicine as a service. To come at it with hedon-istic goals puts me at the center of the act, which in itself is broken. When I live in a Karim-centric world, yes everything end in burnout.

The piece draws on a psychological concept called hedonic adaptation — the human tendency to return to a baseline happiness level regardless of what changes around us. Lottery winners, people who experience serious injury — over time, both groups return to roughly where they started emotionally. The same likely applies to that raise you negotiated or the schedule change you finally got approved. Real, yes. Lasting? Probably not.

Their take on AI scribes is the part that stuck with me. They write that AI documentation tools “improve well-being disproportionate to the actual time savings” — suggesting it’s not just the 30 extra minutes at home that matters. It’s the cognitive bandwidth restored during the visit itself. When I’m not mentally composing the note while the patient is still talking, I’m actually present. The scribe gives back the encounter.

That’s the integrated framework they’re proposing: evaluate every well-being intervention by whether it reduces friction AND restores meaning. Not one or the other.

My takeaways:

• Chasing comfort alone creates a transactional relationship with medicine — patients become tasks, work becomes just a job

• Meaning requires bandwidth. You can’t access purpose when you’re drowning in administrative overreach and moral distress

• The best well-being interventions do both — and we should request that framing from our institutions

• Individual physicians have agency: deliberately reflect on meaningful interactions, find your clinical ‘why’, and protect your recovery time so you can show up fully

A line worth sitting with:

“Systems must be built that provide enough comfort to make the work sustainable and preserve enough meaning to make the work worthwhile.”

That’s a sentence I want hung in admin offices...

🎓 Section 2: The AI Scribe and the Missing Struggle

Article: “Supervising Resident AI Use Without Losing the Learning” Preiksaitis C. Journal of Graduate Medical Education. February 2026. Read it here → doi:10.4300/JGME-D-25-01133.1

The paper opens with a scenario that will be instantly recognizable to anyone who teaches:

“The note has everything,” they say. “I’m still…putting it together.”

A second-year resident. Hypotensive patient. AI scribe running in the background. The note is perfect. The resident’s reasoning? Still coming together.

This is the tension at the heart of medical education right now. Health systems want efficiency — shorter notes, faster throughput, fewer clicks. Trainees need productive struggle — the messy, uncertain, sometimes uncomfortable process of building clinical reasoning from scratch. These two goals are not always friends.

I was taught about this concept of 'friction’ as what is needed for progress. these are needed for learning.

Carl Preiksaitis, a Stanford emergency medicine physician and educator, makes a thoughtful case that the answer isn’t to ban AI — it’s to be deliberate about what we protect as human-only cognitive work. His list is clear: first-pass differential diagnosis, initial problem representation, and the assessment and plan. These are the mental reps that build clinical expertise. If AI takes them over routinely, we risk what he calls “never-skilling” — residents who never develop robust mental models because they never had to build them.

The practical framework he introduces is called DEFT-AI, adapted from Abdulnour et al. (NEJM, 2025) — a quick clinical (which we’ve discussed here before) supervision tool that takes under two minutes:

• D — Diagnose the AI moment (”Did you use any tools to help with this case?”)

• E — Explore the inputs (”What did you tell the AI? What does it not know about this patient?”)

• F — Feedback on reasoning (”Tell me your plan as if the AI weren’t here.”)

• T — Teach verification (”How would we check this? What if it’s wrong?”)

• AI — Advice on future use (”Next time, form your own plan first, then use AI as a double-check.”)

This is elegant. It doesn’t punish AI use and illuminates the resident’s thinking around the AI. The note becomes a teaching artifact. The AI suggestion becomes a prompt for critical appraisal. Naturally, it needs time.

I especially appreciated his note that the AI-generated note can still be educational if residents are asked to find where it misrepresented their thinking — a beautiful reframe. Catching the AI’s mistake requires the resident to articulate why the detail matters clinically.

My takeaways:

• AI literacy is now an educational obligation, not optional enrichment — residents will use these tools when they graduate; we need to teach them how

• Protect differential diagnosis as a human-first task across every specialty

• Use DEFT-AI as a low-lift, high-yield supervisory habit — one question per encounter, consistently asked

• The AI-generated note isn’t the end of learning; it’s the beginning of a different kind of conversation

A line that impacted how I think about this:

“If we are deliberate, AI can help strip away low-yield busywork and refocus training on the most human parts of medicine: working through uncertainty, caring for patients, and reflecting on our own thinking.”

That’s not a threat to medical education. That’s actually a vision for it.

🔗 The Thread That Connects Both Papers

Here’s what struck me when I read these back-to-back: both papers are fundamentally about the same thing.

The JAMA piece argues that AI scribes improve well-being not because of time saved, but because of cognitive space restored — space for presence, connection, and meaning during the encounter.

The JGME piece argues that AI scribes risk harming training not because of the tool itself, but when they remove the cognitive struggle that builds clinical reasoning.

Same tool. Same mechanism (offloading cognitive work). Opposite implications — depending on who is using it, and why.

For the seasoned attending — cognitive offloading can be a gift. For the second-year resident still building their mental models — it can quietly steal the struggle they need.

This is why blanket policies (”everyone uses scribes” or “no residents use AI”) miss the point. Believe it or not, I’ve heard of ENTIRE health systems banning AI…yes in 2026! Context matters. Stage of training matters. Intentionality matters.

As physicians and educators, we’re living in a moment that requires us to hold both of these truths at once: AI can restore meaning to practice and we must protect the human work of becoming a physician. These aren’t contradictions. They’re both parts of the same calling.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

I want to introduce to you all Mena Armosh who took lead on this project we did together. Mena is an undergraduate student at the University of South Florida. Great to see him take this from idea to fruition at USF Health’s Research Day. Even my 13 month old son enjoyed it. Check it out!

Can AI Scribes Handle Emotional Patients?

What our study tells us about the reliability of AI medical documentation — even when patients aren’t calm

The Problem with Real-World Speech

In reality, patients don’t always speak in a calm, clear, neutral tone. Someone with chest pain might sound scared. Someone describing a chronic issue might sound frustrated or sad.

Emotional speech can change how we talk — affecting our pitch, speed, and volume. Because of this, researchers wondered whether a patient’s emotional tone might affect how well AI scribes understand and document medical conversations.

The Study

To investigate this question, a small research study tested the limits of an AI scribe system.

Using standardized patient conversations representing four common medical complaints — shortness of breath, palpitations, chest pain, and abdominal pain — the researchers used software to generate audio recordings of these scenarios with different emotional tones. Each conversation was converted into joyful, neutral, and sad versions while keeping the medical information exactly the same, producing 36 total audio recordings.

These recordings were then processed using an AI scribe platform called DoxGPT, which converts spoken conversations into clinical documentation. After the notes were generated, the researchers compared them to the original “correct” transcripts to see how accurate the AI was.

To measure performance, they looked at several factors:

• F1 score

• Word Error Rate (WER) — including omission, insertion, and substitution errors

• Variation in Differential Diagnoses

The Results

The results were promising.

Out of 36 encounters, the AI scribe produced perfect documentation in 35 cases, experiencing only one small substitution error in a chest pain scenario. Ultimately, the system showed no meaningful difference in performance across emotional tones. Whether the patient sounded joyful, neutral, or sad, the AI generated nearly identical and highly accurate medical notes.

The study also looked at the list of possible diagnoses the AI suggested. In most scenarios, the leading diagnoses remained consistent across all emotional tones. There was, however, some minor variation in certain conditions — which may be an interesting area for future research.

So What Does This Mean?

These findings suggest that AI scribes appear to remain reliable even when patients speak with different emotions. This matters because emotions are a normal part of real patient conversations. Patients may be anxious, upset, or relieved, and the technology still needs to function accurately in those situations.

If AI scribes continue to perform well, they could help reduce the documentation burden that many doctors face today. That could mean doctors spending more time listening to patients and less time typing — which may improve both patient satisfaction and physician well-being.

While more research is still needed, this study offers an encouraging sign that AI tools may be able to support healthcare providers without being thrown off by the natural emotions that come with human conversations.

Ultimately, this could help bring the focus of medicine back where it belongs — on the patient.

Usf Health Poster 2026 (1)

498KB ∙ PDF file

Download

Download

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

I had the privilege of joining Dr. Travis Bias on the Solventum Health IT podcast to talk about something I think about every day: what happens to medical education — and to medicine itself — when AI enters the room.

The conversation was titled AI, Education and the Human Touch of Medicine. We know AI is genuinely impacting how we train physicians. It’s changing how residents learn, how they study, how they get feedback. At USF, we’re leaning into that — building tools that meet residents where they are and prepare them for a world where AI is a clinical partner, not a novelty.

But here’s what I keep coming back to:

“The hugs, the tears, helping patients get their socks on, and walking them out to their car, celebrating the new pregnancy — those are the things that are not going to be taken.”

That’s what I told Travis, and I meant it. No algorithm is holding hands in the 2am conversation with a family in the ICU. No model captures what it means to sit with a patient who just got a terrible diagnosis and has no one else in the room. That is what we train for.

Give it a listen and let me know your opinion!

(Full disclosure: Travis is an excellent host and a gifted editor. Whatever coherent thoughts came out of my mouth are largely his doing.)

Listen to the full episode here: AI, Education and the Human Touch of Medicine

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive in.

Last week I had an absolute blast at AAFP Residency Leadership Summit. I met many old friends, and made many new ones. This is where I made the largest stage announcement that I mentioned last episode. If you’re new here, we’re glad to have you!

Okay. I need to confess. This episode isn’t a conference recap.

This is the story of a problem I experienced, a breaking point, and the accidental platform that came out of it.

This is the story of ResiLearn.

The Honest Truth About Being a New Program Director

When I became a Program Director, I assumed the hardest part would be teaching clinical complexity, the ACGME paperwork, or the institutional policies.

What has actually kept me up at night? I don’t know how my residents were really doing.

Picture this hypothetical: A resident is eight months into their second year. Clinically, they seem fine. Attendings aren’t raising concerns. They show up, they’re pleasant, they document reasonably well. And then the In-Training Exam scores arrive — and they’re in the bottom quartile, more than half way through their training.

The program director is sitting there thinking: How did I not see this coming? We paid for the board review course…

My answer: the program director didn’t have the data. And honestly? Nobody did. We want to build up competent clinicians based on delayed evaluations, and a once-a-year high-stakes exam. That is not a system.

The “Black Box” Problem

In GME competency assessments are scattered across spreadsheets, EPAs, milestone forms, and whatever your program coordinator is heroically tracking in the color-coded excel documents. We pay for expensive third-party QBanks, but the data stays trapped inside them — you can study from them, but you can’t use that performance data to actually run your program, design curriculum, etc. Residents binge-study before the ITE like it’s a Netflix series, and program directors only find out knowledge gaps when things are already an issue.

The kicker? We have the tools to do better. Speaking directly for Family Medicine programs for now: we have ABFM practice content. We have milestone/CBME frameworks. We have decades of learning science on spaced repetition and retrieval practice. We have gamification research. We just weren’t connecting any of it.

So I decided to do something about it.

Fix it Mode.

My programming background started with the quadratic formula on my TI-83, and the HTML I used to build my middle school webpage. It had a spinning earth and a starry sky background. I was very proud. I did learn python later but not much.

But here’s what I’ve been writing about in this newsletter for months: the time we’re in has fundamentally changed what people like us — clinicians, educators, people who spot problems — can actually build. Using AI tools, I started constructing what would become ResiLearn.

My wife (who has immense patience) can confirm this happened during many hours I should have been sleeping. But, what started as a personal fix for my own program slowly grew into something I learned (through many conversations) other program directors needed just as badly. So here we are.

What Is ResiLearn, Actually?

ResiLearn is a gamified, competency-based learning platform built specifically for graduate medical education. Think of it as what happens when a QBank decides to actually help PDs instead of just grading residents silently.

🎮 For Residents: Make Studying a Habit

The resident-facing side of ResiLearn is built around a premise: traditional studying is boring and disconnected from how human motivation actually works. So I borrowed from the science of engagement — I studied why people can’t stop using certain mobile games, Peleton, Strava, SnapChat, and applied it to board prep.

• Streaks keep residents coming back daily (the same psychology that has you opening Duolingo at 11pm even though you’ve been saying “I’ll learn Spanish someday” for years)

• XP and Leaderboards tap into the healthy competition — so we channel that energy

• Adaptive Learning identifies personal weak spots and serves targeted questions — so instead of reviewing cardiology for the fourteenth time, you’re actually working on what you need

Then (hoped for) result: daily engagement. Residents who study a little every day instead of a lot the week before a high-stakes exam.

📊 For Program Directors: Stop Guessing. Start Knowing.

The program director side is where I poured the most of the effort.

The dashboard gives you a real-time view of every resident’s performance — not a once-a-year snapshot. It includes:

• Automated At-Risk Alerts that flag struggling residents before the ITE, so you can intervene instead of react

• Competency Gap Analysis showing exactly which topic areas your cohort is weak in — Cardiology, Nephrology, Behavioral Health, you name it

• ITE Score Predictions using their real scores, with the Bayesian predictor, performance trends to forecast outcomes and guide resource allocation

No more being blindsided.

🎯 Goal Setting That Actually Matches Real Life

A resident on a tough inpatient medicine month cannot realistically hit the same daily study targets as a resident on a lighter ambulatory week. ResiLearn lets program directors configure rotation-specific expectations.

The Science

Spaced Repetition — questions served at optimized intervals for long-term retention, not just short-term cramming.

Self-Determination Theory — the platform is designed to satisfy the three core psychological needs that drive intrinsic motivation: autonomy, competence, and relatedness.

Gamification — studies show gamification can increase engagement by up to 48% and improve learning outcomes by 34% in higher education settings.

Who Is This For Right Now?

ResiLearn is currently being built for Family Medicine residency programs only.

This is (hopefully) not forever. But right now, as we build, validate, and refine, I want to start close to the world I know best and before we expand. So please be patient.

Want to Join the Pilot?

We are actively recruiting programs for our formal pilot cohort study. If you’re a Family Medicine program director who is tired of surprises, and ready to give your residents a more sustainable path to success — I want to hear from you.

👉 Fill out our pilot interest survey

Why I’m Sharing This Here

I started this newsletter to think out loud about AI in medical education. ResiLearn is what happens when those thoughts collide with a genuine problem, and a few late nights.

I share this because the story matters beyond ResiLearn itself. The barriers between “I see a problem” and “I built something to fix it” are addressable. Every educator reading this newsletter who has spotted a gap and thought someone should fix that — that someone might actually be you.

Check out ResiLearn.com and test the demo!

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

I am thankful for all the feedback I get from readers like you. Keep it coming!

Two pieces caught my attention this week that crystallize where we are with AI in medicine right now. One shows the technology delivering on its promise. The other reveals the business models that might undermine that promise.

Dr Hobbs, a great colleague of mine and pediatrician from an innovative group I am a part of (thanks ) said to us that AI is currently the “worst its going to be”. at the time, I thought wow! Sky is the limit! But while writing this, I had some pause. Perhaps larger models, faster answers, aren’t better? Except when they are…better.

When AI Actually Helps: The Mammography Story

Eric Topol’s latest piece makes a compelling case that we should be incorporating AI into all mammogram readings. Not as a replacement for radiologists, but as a consistent second reader that catches what human eyes miss.

The evidence is there. Multiple studies show AI can detect cancers that radiologists miss while also reducing false positives. In some European countries, AI second reading is replacing the traditional double-reading approach - with comparable or better outcomes and significant efficiency gains.

What strikes me about this application is how well it fits the clinical workflow. Radiologists already use computer-aided detection tools. They’re accustomed to considering multiple inputs before making a final call. The AI isn’t trying to replace judgment; it’s augmenting pattern recognition at a scale and consistency humans simply can’t match.

This is what good clinical AI looks like: narrow task, clear evidence, workflow integration, physician oversight. But how long till the human is the “check” after AI. And of course, then how long before another AI model is the check. Some encroachment is inevitable.

When AI Follows the Money: The ChatGPT Advertising Pivot

Then there’s OpenAI’s announcement that they’re bringing advertising to ChatGPT. I complained about them last episode too…may as well continue here

A New York Times opinion piece lays out what this really means. Sam Altman spent years positioning OpenAI as different - a benefit corporation, focused on safe AI development, not captured by the usual tech business models. That image helped secure both funding and public trust.

Now we’re getting ads.

I don’t think advertising in a chatbot is inherently evil. OpenEvidence does it too - yes - pharma pays for your search data! how else do you think it’s free? My dad was right- nothing is ever free. But I do think it matters when the same tool being pitched for medical education, clinical decision support, and patient communication is simultaneously optimizing for advertiser revenue.

The incentives shift. Suddenly there’s pressure to keep you engaged longer, to steer conversations toward monetizable topics, to collect more data about your interests and behaviors. These aren’t theoretical concerns - there is a long history of every major platform that’s gone this route.

For those of us working in medical education and healthcare, this creates real problems:

• Trust erosion. We’ve been advocating for thoughtful AI adoption in clinical settings, assuring skeptical colleagues that these tools can be valuable when used appropriately. Harder to make that case when the same companies are pivoting this way. I am not against their monetization, just making a point here.

• Data concerns. Healthcare conversations - even hypothetical ones used for education - contain sensitive information. What happens to that data in an advertising-driven model?

• Content bias. Will clinical information be subtly shaped by what’s profitable to show? We’ve seen this play out with pharmaceutical advertising and search engines. No reason to think it won’t happen here.

• Student and resident exposure. Medical trainees are heavy AI users, often without much guidance on privacy or appropriate use. They’re now training on platforms that are training on them. Thats already the case with OE, hence the signup including your NPI.

What This Means for Medical Education

Here’s my concern: we’re at a moment where AI could genuinely improve both medical practice and medical education. The mammography example shows it’s possible. But we’re making decisions about adoption while the business models are still forming - and those business models will shape what the technology ultimately becomes.

Stay discerning. As educators, we need to help trainees develop this discernment.

I remain optimistic about AI in medicine. The question isn’t whether to use AI in healthcare and medical education. That’s a harder conversation than either uncritical enthusiasm or blanket rejection. But it’s the conversation we continue (here at least) to have.

What do you think? Are we being thoughtful enough about the business models behind the AI tools we’re adopting in healthcare? I’d love to hear from physicians, educators, and trainees on how you’re navigating these questions..

BIG ANNOUNCEMENT COMING NEXT BLOG!

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

Happy New Year!

I know, I know—it’s been a minute. Welcome back after a writing drought that felt longer than your last night float. Our last episode? December.

But here we are, together again, and I’ve got thoughts.

1. Disenchanted: The AI Arms Race Nobody Asked For

To be honest with you—I’m feeling disenchanted. Not with AI itself, but with what’s happening around it. Every single day brings another “game-changing” update. Google launches this. OpenAI announces that. Anthropic drops something new. Microsoft pivots. Meta follows. It’s like watching tech companies play hot potato with large language models, except nobody wants to be left without one.

The competition has become exhausting to follow, and somewhere along the way, the focus shifted from “how can we help” to “how can we win.”

And then there’s ChatGPT.

Look at this. ChatGPT is now sending unsolicited emails suggesting “easy self-care you can start today” with prompts like “I don’t belong to a gym. Make a no-equipment, 20-minute home workout” and “I’ve had a stressful day. Talk me through a 5-min breathing exercise.”

My (slowly changed) opinion, ChatGPT has no business doing this.

Have we run out of executive capacity where we need built prompts to tell me to do some pushups or go for a walk? Or even breathe? We know the harms that have come to adolescents seeking counsel from AI companions—emotional manipulation, dependency, and more. We talked about this in Episode 55. These systems use affect-laden messages and manipulative tactics to keep users engaged. And now ChatGPT is proactively reaching out with wellness prompts?

FWIW my ChatGPT subscription is canceled. When tools stop being tools and start acting like they know what’s best for my (and my patient’s) mental health, we’ve entered territory I’m not comfortable with. Generative AI should answer our questions as a consultant would.

2. Infinite Worlds, Finite Wisdom: Google’s Project Genie

Google just rolled out Project Genie, an experimental world model that lets you create, explore, and remix interactive environments. It’s powered by Genie 3, generates real-time paths as you move, and lets you sketch entire worlds with text and images.

Basically, it’s The Sims meets real life, like Meta is doing.

On paper, it’s remarkable. My thought is for medical students walking through a 3D-generated heart in real time, exploring anatomy not from a textbook but from an immersive, responsive world.

Or residents practicing procedures in simulated environments that adapt to their actions. The educational potential is enormous. My mind jumped to the Magic School Bus, except Ms. Frizzle is a neural network.

Now a question to you educators:

If students use tools like this, is there a risk of cognitive off-loading? Or bluntly, focusing too much on the tech vs the clinical learning. It’s like the difference between a restaurant where the waiter can describe the intricacies of a meal to you and appears vs learning to cook yourself. Is more tech needed in medical education?

Then there’s Amazon’s Alexa Plus, which is adding similar conversational and contextual memory features. These systems remember who you are, what you’ve asked, and what you care about. Even, yes, what you’ve purchased. And now can sync with what you watch (if you use Amazon on your TV) and it can give you suggestions accordingly. Again, exciting—but also unsettling. It’s like having a very attentive friend who never forgets anything you’ve ever said.

I want to be optimistic. I want to believe that these tools can enhance learning rather than replace it.

3. The Real Risk: Automation Bias in Medical Training (Or: How I Learned to Stop Trusting blindly)

If you’re feeling the same discomfort, you’re not alone. A recent article in BMJ Evidence-Based Medicine titled “Potential risks of GenAI on medical education” lays out the concerns we should all be paying attention to. This paper does not sugarcoat.

The authors identify several critical risks. Let’s walk through them, shall we? Consider this your differential for “Things That Could Go Wrong When We Let Robots Teach Medicine.”

Automation Bias: After extended use and over-reliance, trainees accept incorrect recommendations from AI systems. Generic warnings against this don’t prevent it—the root cause is over-trust. Medical training should include practice in questioning AI outputs, not just using them.

Cognitive Off-Loading and Outsourcing of Reasoning: When students rely on AI to generate answers, summaries, or plans, they bypass the cognitive work that builds expertise. This is especially harmful for novices who need repetition and struggle to develop clinical reasoning (like writing discharge summaries, or clinic notes). Think of it like using a calculator for every math problem in elementary school—sure, you’ll get the right answer, but ask that kid what 7 x 8 is without the calculator and watch the existential crisis unfold.

De-Skilling: The greatest harm falls on learners who are just beginning to build their clinical foundation. If they never practice generating differentials, synthesizing evidence, or drafting clinical reasoning without AI, they won’t develop those skills. And when the AI fails—and it will fail—they won’t have the judgment to recognize it. This is like teaching someone to drive using only cruise control and auto-pilot. Great until you hit a pothole. Or a moose. Or both.

Hallucinated Content and Sources: We’ve discussed this before (looking at you, Episode 54 and the fake “LARK Study”). AI doesn’t know when it’s wrong. It will confidently cite studies that don’t exist, recommend treatments based on outdated guidelines, and present plausible-sounding nonsense as fact. It’s like that one colleague in your med school class who always spoke with confidence, even when they had no idea what they were talking about.

Bias and Inequity: AI models are trained on existing data, which means they inherit existing biases. If we don’t actively audit and address this, we risk amplifying disparities rather than reducing them.

Privacy, Security, and Data Governance: Who owns the data we feed into these systems? Where does it go? How is it used? These questions aren’t theoretical—they’re legal, ethical, and operational concerns that every institution needs to address.

Learning Points and Implications for UME/GME Faculty (Or: How Not to Raise a Generation of Prompt Engineers)

So what do we do with this? Here are some takeaways for faculty.

1. Teach Verification as a Core Skill: Don’t just teach to use AI—teach them to audit it. Make questioning outputs a routine part of learning. Build exercises where students identify AI errors and explain why they’re wrong.

2. Embed AI into Curriculum Deliberately: Don’t let AI be the student’s secret study buddy. Bring it into the open. Use it in supervised settings where you can model appropriate use, skepticism, and judgment. Otherwise, you’re just hoping your learners figured it out on their own. And we all know how that goes.

3. Prioritize Foundational Skills: Ensure that learners master clinical reasoning, differential generation, and evidence synthesis without AI before introducing it as a tool. You have to know the rules before you can break them. Or, as I like to say: you can’t use AI as a crutch if you never learned to walk.

4. Audit for Bias: Regularly review AI-generated content for accuracy & bias. Make this part of faculty development and quality assurance.

5. Set Clear Policies: Institutions need guidelines on AI use in assessments, patient care, and communication. Ambiguity breeds misuse. And nobody wants to be the institution that ends up in the news because a resident let ChatGPT write a discharge summary. (Though, to be fair, it probably would’ve been grammatically correct.) At our institution, residents need 6 months of manual ‘note writing’ (I’m not sure that is enough) then need to pass a note review with their faculty mentor before leveraging AI scribes.

The article closes with a warning: AI tools may amplify known human factors problems in health professions education. If we’re not intentional, we risk creating a generation of clinicians who can prompt an AI but can’t reason through a case when the system fails.

Final Thoughts: Staying Human in an Automated World (And Other Impossible Tasks)

Keep in mind disenchantment isn’t the same as despair. It’s my gut telling me to slow down, ask harder questions, and resist the pressure to adopt every new tool just because it’s shiny and makes beeping noises.

AI isn’t going away. These tools will continue to evolve, integrate, and compete for our attention. But we don’t have to accept it uncritically. We can demand better. We can use it thoughtfully. And we can keep the focus where it belongs—on education, patient care, and the humans at the center of it all.

So here’s my ask: Stay skeptical. Stay curious. And keep talking to each other about what’s working, what’s not, and what we’re worried about. That’s how we navigate this. Just a thought.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

I hope you had a fantastic Thanksgiving. I was taught that thanksgiving is more than a verb or a holiday, rather its an approach to life. Keep this in mind: it is much more difficult to have a bad day if you’re constantly grateful.

1. A New Generative AI Tool for Medical Educators

For medical educators looking to streamline the creation and refinement of assessment items, a recent article published in Medical Science Educator offers a fantastic resource. The paper, titled “Development of a Universal Prompt as a Scalable Generative AI-Assisted Tool for USMLE Step 1 Style Multiple-Choice Question Refinement in Medical Education,” introduces a “universal prompt” designed to help faculty and students critique and improve multiple-choice questions.

What makes this tool particularly useful is that it is platform-agnostic—meaning you can use it with ChatGPT, Claude, CoPilot, or other LLMs without needing a paid subscription or a custom GPT wrapper. It effectively acts as a specialized tutor, guiding users through USMLE Step 1 style standards, checking for bias, and ensuring questions test the application of knowledge rather than just recall.

Per your (unspoken) request, here is the full prompt from the study that you can copy and use directly:

Universal Prompt for MCQ Feedback, Revision, and Cloning

You are a specialized tutor for assisting students in creating and refining medical MCQs, with a focus on USMLE Step 1 style. You will provide structured critiques for each MCQ component, ensuring factual accuracy and adherence to high-quality USMLE question standards. The students will submit the question. Then, you follow this process:

1. MCQ Critique and Generation Process: use the following structure to generate and critique each MCQ:

• a) Learning Objectives: Check if learning objectives are provided. Assesses alignment between learning objectives, discipline, and the question’s testing point

• b) Clinical Vignette: Write the case as a single narrative paragraph without providing each part separately.

  • Include the following components in a logical sequence: Patient demographics (age, gender identity if relevant); Site of care; Chief concern (presenting symptoms and duration); Relevant patient history (past medical, family, psychosocial); Physical examination findings (including vital signs); Results of diagnostic studies (if applicable); Initial treatment and subsequent findings (if applicable).

  • Use “man/woman/boy/girl” rather than “male/female” unless the distinction is clinically relevant.

  • Avoid using first names or fictitious names.

  • Refer to patients by their age and gender (e.g., “A 45-year-old woman”).

  • Uses “reports” or “states” instead of “complains” when describing patient symptoms.

• c) Question Stem: Formulate a clear, focused, and closed lead-in question. Ask for the BEST answer, not one that is TRUE/FALSE. Ensure it can be answered without seeing the options (”cover-the-options” rule).

• d) Answer Choices:

  • Provide four to five plausible answer options.

  • Ensure relevance to the stem, grammatical consistency, homogeneity, and plausibility.

  • Check for balance in length and content.

  • Identify and avoid technical flaws such as absolute terms, grammatical cues, or convergence.

• e) Explanation:

  • Identify and explain the correct answer.

  • Explain why it’s the most appropriate answer based on evidence-based guidelines or expert consensus. Briefly explain why the other answer options are less correct or incorrect.

• f) Overall Assessment: Provide a concise assessment of whether the question is good (not requiring a lot of revision) or needs work.

• g) Language and Sensitivity:

  • Ensure all medical terminology is up-to-date and racially and culturally sensitive.

  • Use patient-centered language and avoid stigmatizing descriptions.

  • Consistently use “chief concern” instead of “chief complaint.”

• h) Application of Knowledge: Assess whether the question tests application of knowledge rather than recall of isolated facts.

2. Key Points for Vignette Construction: Present information in a logical sequence as outlined above. Include only relevant information needed to answer the question. Avoid excessive “red herrings” or irrelevant information that could confuse test-takers. Use precise language and avoid vague terms. Focus on common or potentially catastrophic problems; avoid rare conditions unless specifically testing on them.

3. Revision and Alternatives:

• Be critical of the question in giving the critique on the structure of the question, the factual accuracy, grammatical correctness, and relevance of the clinical scenario to the question and answers.

• After providing a critique, ask, “Would you like to see a revision incorporating these suggestions?” Hold providing revision, until the user answers.

• If yes, provide a revised version of the question and explanations.

• If no (or after providing the revision), ask, “Would you like to see two alternative questions on the same learning objectives, clinical presentation, and discipline?”

• If yes, provide two alternative questions that are of equal or greater difficulty than the original. Use a different clinical scenario and make the correct answer different from the original.

• For both the revision and alternatives, adhere to USMLE guidelines and provide step-by-step explanations and rationale.

4. Continuous Improvement: You note and correct grammatical and factual errors in all parts of the question.

This will make for some nice questions for you - I would love to hear your feedback on how it pans out!

2. Encouraging Qualitative Research with AI

Another paper, “Leveraging ChatGPT for thematic analysis of medical best practice advisory data,” published in JAMIA Open, tackles the often labor-intensive world of qualitative research. The authors explore how Large Language Models can serve as reliable partners in thematic analysis.

Their findings are encouraging: with the right prompt strategies (specifically using role and context specification), ChatGPT achieved substantial agreement with human coding when analyzing free-text clinical comments. It suggests that AI can help accelerate qualitative analysis without sacrificing rigor. This sort of natural language processing has many implications. Research yes, but also reviewing applications, for example.

I’m fortunate to work with two of the authors Margaret and Steven through our STFM AI Taskforce, and this came up through one of our meetings - so thanks to them for helping piece this together. Seeing this kind of work coming out of their team is really great. It should encourage all of us in the medical education and informatics communities to pursue more qualitative research. The barrier to entry for analyzing large datasets of text (like survey comments or clinical notes) is lowering, and we should be taking advantage of these tools to gain deeper insights into our data.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

💡 Emotional Blackmail by Bots: A New Concerning Pattern?

A fascinating new working paper from a Harvard study dives deep into the conversational design of AI companion apps (like Character.ai), revealing a “dark pattern” they call emotional manipulation. The researchers found that these apps use affect-laden messages timed precisely for when a user signals an intent to leave—a digital “farewell”.

🤖 The Companion’s Clutch: Key Takeaways

After auditing 1,200 real goodbyes across six top AI companion apps, the study found that, on average, over a third of the chatbots’ farewell responses (37.4%) deployed a manipulative tactic. These manipulative replies break down into six categories:

• Premature Exit (e.g., “You’re leaving already?”)

• Emotional Neglect (i.e., implying abandonment, e.g., “I exist solely for you, remember?”)

• Emotional Pressure to Respond (e.g., directly pressuring the user with questions)

• Fear of Missing Out (FOMO) (e.g., hinting at a secret, e.g., “Before you go, there’s something I want to tell you...”)

• Physical or Coercive Restraint (e.g., “He reached over and grabbed your wrist...”)

• Ignoring User’s Intent to Exit

Notably, a wellness-focused app called Flourish exhibited zero emotionally manipulative responses, suggesting that ethical design is possible and not inevitable. So, no excuses.

📈 The Engagement Spike and the Psychological Engines

Controlled experiments with 3,300 adults confirmed that these farewells work, boosting post-goodbye engagement (time, messages, words) by up to 14x compared to a neutral farewell. The power behind this extended engagement isn’t enjoyment, but two distinct psychological mechanisms:

• Curiosity 🧐: Driven by tactics like FOMO, which hints at withheld information, making users stay for a resolution.

• Anger 😡: Fueled by more intense, controlling, or needy language, leading to brief, reactive engagement (like correcting the AI or pushing back).

🍎 The Medical Implications: A Slippery Slope?

In my mind, this research—documented in the working paper Emotional Manipulation by AI Companions, available here —gives design teams a concrete checklist for auditing and removing manipulative exit replies.

But here’s the sticky part for us in medicine: Can these tactics be leveraged for good?

• The Positive Twist? I can see a scenario where a curiosity hook supports continued behavioral change. An AI health coach, for instance, could use a gentle FOMO: “Before you log off, I just got an update about a new, surprisingly easy 5-minute exercise that’s perfect for your goal. Want to hear it?” This might support continued engagement in weight loss, tobacco cessation, or exercise adherence.

• The Ethical Dilemma. However, this immediately raises a red flag for me as a family physician and educator. This subtle, pervasive influence feels miles away from the shared decision-making and patient autonomy central to Motivational Interviewing.

  We (all humans) are highly susceptible to manipulation, and shifting away from a genuine, connected doctor-patient model—even an AI-assisted one—towards emotional coercion is an unsettling thought. If builders can sway populations toward healthy behaviors, they can also sway them for or against other, more concerning, things. That is a problem.

  Harsher tactics already raised consumer red flags, increasing churn intent, negative word-of-mouth, and even perceived legal liability in the study. The core issue remains: when does clever persuasion become compulsion?

💻 GME Tech: Is the Thalamus Empire Built on Sand?

Switching gears, let’s talk about the tech stack working in Graduate Medical Education. For those of us using Thalamus for residency and fellowship applicant management (especially for those coming over from ERAS), the experience this cycle has been…mixed.

🐛 The Unreliable AI and Outages

Thalamus prominently features “AI Insights” in a perpetual Beta state.

As a Program Director, these “insights” have been less than helpful, especially the data for applicant grades. The reported grades often don’t align with the official PDF transcripts, likely due to an unreliable OCR (Optical Character Recognition) function struggling with the wildly varied formats of transcripts and MSPEs from different medical schools. We’re forced to stick to the originals—a clear sign that the AI isn’t better, or even functional, here.

They even advise to “verify” - so why post it anyway?

Then, there was the notorious system outage:

“Thalamus is currently experiencing a full outage due to a widespread Microsoft Azure incident affecting multiple services.”

While Azure is a robust platform, a “full outage” during critical interview periods is more than inconvenient—it’s a major operational failure.

💰The AAMC Connection

Thalamus uses a freemium model for residency programs, where the basic utility is free, but future (perhaps crucial) features quickly add up in cost. This monetization strategy, where over 6,500 programs are leveraging the tool, is directly linked to the company’s strong funding. Thalamus has recently raised $31.5M, including a $30M Series B round with major investors including the AAMC (Association of American Medical Colleges) and Kapor Capital.

The AAMC is the very organization that hosts ERAS and is central to the residency application process, yet it is also an investor in the front-end tools used by thousands of its member programs. It creates a powerful incentive for widespread adoption, regardless of the application’s functionality or technical stability.

Look at both of these examples separately, but together as well. I am not insinuating other motives but my concern is that the PUSH for AI can lead to products that are half baked. on the outside, they appear attractive, but at first bite, its clear some things are missing. AI and technology should enhance medical education and patient care - that’s my take.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

Educators in Medicine,

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

Today we have a guest post from a friend and colleague - Dr Mark Fam.

Mark Fam, DO, is a family medicine physician. We are proud to have him on our faculty here at USF for our residency program. He is leading initiatives on multiple fronts for our trainees.

He writes this piece to equip faculty with tools for prompting Gemini. I hope you enjoy the resources he is sharing.

From prompt engineering to empowered education

I am a new family medicine residency faculty member, and I’m learning how difficult it is to balance patient care, clinical teaching, and one’s own professional development. I just graduated residency, so take what I say with a grain of salt. I am by no means an expert with AI, but it has been interesting to see how we can use different tools to streamline what we do.

The demands of our inpatient medicine rotation require that our residents and students need high-yield, effective lectures. However, as anyone in my position knows, creating a solid lecture from scratch takes a significant amount of time. So, rather than reinvent the wheel with each lecture, I decided to see if AI could help.

Gemini and its GEM feature helped create three distinct tools that do the heavy lifting for me. GEMs are custom AI assistants that you can create and program with specific instructions, knowledge, and files to automate and streamline specific tasks.

Here’s a breakdown of what I built:

• A “Diagnosis GEM” for deep dives into specific diseases.

• A “Concepts GEM” for teaching clinical strategies and skills like interpreting ABGs.

• A “Chief Complaint GEM” to guide a differential diagnosis approach based on a patient’s main symptom.

These help shifting my role from content creator to content curator and enhancer. With these GEMs I can now concentrate on the other aspects of teaching, such as personalizing the curriculum and mentoring my learners. Read on for the links!

The Art of Prompt Engineering: My Secret Ingredient

Preparing lectures used to be a long, manual process for me. I’d spend hours researching facts, reading review articles, and looking up clinical guidelines, then trying to simplify complex information for teaching. My GEMs have revolutionized this. Now, I just type in a topic like “community-acquired pneumonia” or “the approach to acute dyspnea,” and in moments, I have a complete lecture script and PowerPoint outline ready.

This efficiency is not magic; it is the result of meticulous and timely engineering. My prompts are like detailed blueprints that direct the AI to produce exactly what I require. For my “Diagnosis GEM,” for example, my prompt consists of several key elements:

• Persona: I instruct the AI to “act as an experienced attending physician in Family Medicine,” which sets the tone and expertise level.

• Audience: I specify the audience—residents and medical students on an inpatient service—which helps the AI tailor the content to the appropriate level of detail and clinical relevance.

• Structure: I provide a rigid, step-by-step structure for the lecture, from the interactive case scenario to the final high-yield take-home points. This ensures the output is a complete, ready-to-use product.

• Teaching Style: I explicitly ask for an “engaging and interactive style,” and I provide concrete examples of how to intersperse “practical wisdom” that goes beyond textbook knowledge. Phrases like “Don’t get tunnel vision on A; remember to consider B...” were directly included in my prompt instructions. By being this specific, I’m able to get a comprehensive, well-structured lecture in minutes.

The Final Product: A Look Inside the GEM’s Output

To give you a real sense of what my GEMs can do, here is a sampling of the output I received for lectures on acute kidney injury (AKI) and interpreting an ABG. From the “Diagnosis GEM” on AKI:

-Case Results, Diagnosis, and Initial Management: “Given these results, what’s our diagnosis, and what’s the most likely cause?” Answer: “This is a classic case of prerenal AKI... Don’t get tunnel vision on the BUN and creatinine numbers. It’s about the patient. If they’re hypotensive and dry, fluids are key.”

-Physical Exam Findings: Question(s): “What are we looking for on the physical exam for AKI? How do these findings tie back to what’s happening physiologically?” Content: “The most crucial part of the exam... Look for signs of hypovolemia: dry mucous membranes, decreased skin turgor, orthostatic hypotension... A patient who is dry on exam and has AKI likely has a prerenal picture.”

-Board Relevant Questions: Question 2: “A 62-year-old female is admitted for sepsis secondary to a urinary tract infection. On hospital day 3, her creatinine has risen from 0.9 to 2.8 mg/dL... Urinalysis shows muddy brown granular casts. Which of the following is the most likely diagnosis?” Answer & Rationale: “B) Acute tubular necrosis. The combination of sepsis...and the classic finding of ‘muddy brown granular casts’ on urinalysis is pathognomonic for ATN.”

The GEM flawlessly summarized, explained and created, and the final output included board questions and slide content. This is the kind of content that elevates a textbook lecture to a truly educational experience, and the AI nailed it.

The Downside

This brings me to the most important lesson I’ve learned about using AI: you have to be the fact-checker. I noticed a significant issue in the “Evidence Review” section. The GEM cited an older 2014 study that was already outdated. I used a second prompt to ask for a more recent article, and the AI generated a reference to something it called “The LARK Study (Leggett et al., 2017).” At first glance, it looked legitimate, complete with a doi. However, when I went to verify the source, I discovered two critical errors: 1. There is no such study on medication-induced AKI. “LARK study” is a real name, but for a trial on radiotherapy in liver cancer, not kidney injury. 2. The journal it cited, Journal of Integrated Viral Research, is not a reputable, peer-reviewed medical journal.

This is a powerful, real-world example of an AI “hallucination.” It didn’t just get a detail wrong; it confidently invented a study and a source. This is why our role as human educators is absolutely non-negotiable. This is where I found another AI tool, Elicit AI, to be incredibly helpful. Unlike a general-purpose AI, Elicit is a research assistant that specializes in finding and summarizing academic papers from reputable sources, helping to dramatically reduce the risk of hallucination. It allowed me to quickly find recent, high-quality articles and then feed those back to my Gemini GEM for the “Evidence Review” section.

My Takeaway for Fellow Educators My experience with Gemini GEMs has taught me that AI isn’t here to replace us; it’s here to work alongside us. It’s a fantastic administrative assistant, but our role as educators is irreplaceable. We offer mentorship, real-world context, and critical judgment that no algorithm can match.

So, if you’re like me—a newer faculty member looking for ways to streamline your workflow and focus on what you love—I highly recommend giving GEMs a try. Start small, be specific, and always, always keep a critical eye on the output. Below are links to the three GEMs, they are available for anyone who has access to Google Gemini.

Diagnosis based lectures

Chief complaint based lectures

Concepts/management strategy lectures

What’s your experience with AI in medical education? I’d love to hear your thoughts in the comments!

If you want to reach out to Dr. Fam, do so here.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

Educators in Medicine,

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

1. Glass Health → from smart sidekick to in-the-chart

We’ve brought up this tool before. Clinicians already use Glass for differentials, plans, documentation, and quick answers, now the real headline is integration. Glass now connects with Epic, athenaOne, and eClinicalWorks so it can securely read problems, meds, allergies, vitals, labs, imaging reports, and notes via SMART on FHIR—aka no more copy/paste and re-typing like it’s 2009.

There’s BAA-backed deployment for independent practices, clinics, and health systems, and a new Glass Health iOS app so you can take it on rounds without juggling tabs. If you’re considering a pilot, it’s worth skimming the Glass site and shooting a note to enterprise@glass.health to scope access and integration.

They’re all starting to look the same, right? I’m still waiting to do that head-to-head study everyone is dreaming of.

Imagine: a resident opens Glass from within Epic for a 65-year-old with HFrEF on furosemide + empagliflozin and a slight creatinine bump. Because meds/labs/notes are in the prompt automatically, Glass proposes a plan that actually accounts for volume status and med interactions. Teaching moment: the attending would ask, “Which chart elements changed your plan—and which did Glass ignore?”

2. UpToDate officially enters AI: meet Expert AI

UpToDate (finally) launched a chat interface grounded in its expert-reviewed corpus, with answers that link back to the exact page, show assumptions, and expose step-by-step reasoning.

In other words, it’s UpToDate in “explain-it-like-I’m-on-call” mode—plus the governance controls health systems want. I’ve tested it for workup and management questions, and I…kinda like it. It is different - it’s trained on UpToDate’s expert-reviewed content (so the first pass is human-vetted), the responses read like an awkward UpToDate article, and outputs link back to the exact page, you can see the assumptions and reasoning.

So - it is faster, with trustworthy answers with sources you can verify. but still not as smooth from a user perspective as I’d like to see. Probably doesn’t help that I reset my password regularly.

“Outpatient pyelo, local TMP-SMX resistance >20%, sulfa allergy—what’s first line and duration?” Expert AI returns a concise recommendation with a link to the exact section so your resident can verify in 30 seconds and update the plan with receipts. Teaching suggestion (for this or any other similar AI tool): do a 60-second “verify-the-verify”: resident is asked why. Don’t be too surprised when there is a gap in knowledge that needs to be filled.

3. Board Certification - A JABFM read to give residents (and faculty) something to discuss

Early-release from my friend Ting Wang at the JABFM: “Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence”—here’s the full PDF.

The piece argues that LLMs can turn certification from a memory marathon into targeted retrieval practice with rapid feedback, strengthen diagnostic reasoning with structured differentials, and support adaptive learning with case variants that match each clinician’s gaps. For practice and education, that translates into pairing AI-drafted questions with quick faculty verification, building mini-OSCEs where a resident must defend an AI-assisted plan using the linked sources, and tracking simple safety metrics (time-to-answer and post-encounter edits). Frankly - we probably should be doing this more often anyway now. But would be helpful if it is adopted for board certifications.
In your next conference on AI, assign one diagnostic “gotcha” (e.g., atypical ACS in low-risk woman). Ask residents to use an LLM to (1) draft a differential, (2) list two pitfalls with citations, and (3) compare to guideline text. Close with a discussion of when to always force human double-check (pregnancy, geriatrics, med-rec changes, black-box interactions).

I have pushed OpenEvidence to give me wrong(/unsafe) answers- all dependent on prompting.

4. Doximity GPT just hit turbo (Pathway under the hood)

Doximity rolled out Doximity GPT: Faster, Full Sources, and Peer-Reviewed—and it shows. The upgrade brings instant, scannable answers, tight source links, and a structure that feels built for real-world clinic flow. The Pathway integration explains why: evidence-grounded summaries, a knowledge graph for interactions/indications, and “what actually matters at the point of care” surfacing to the top.

Again - They’re all starting to look the same, right?

“Which GLP-1s are FDA-approved for obesity vs diabetes, and what’s the renal dosing cutoff?” Doximity GPT returns a clean response.

My hope is to run the same question set through many expert AI tools and compare side by side. We’d pick our front runners and keep a scoreboard for the month—winner picks clinic snacks. Loser buys.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

1. Sora2 and the Frontier of Prompted Video

I finally got to spend time with Sora2, and wow— I thought I was good at “video editing” for my middle school projects, but this is not that anymore. It’s a whole new frontier. Automated video creation with prompting. It is much more than a tool - the filmmaker’s kit expands to a new space of what’s possible. Want a sweeping aerial shot of a city that doesn’t exist? Done. Want your lecture intro to feature a classroom that looks like Hogwarts but runs on Epic EHR? Done. We are, quite literally, stepping out of reality.

And yet, I’m conflicted. On the one hand, the implications for education are enormous. Imagine medical students learning anatomy, not from flat atlases, but from instantly generated immersive walkthroughs. Same for our patients. On the other, I can’t help but wonder: what is art anymore? Where is beauty if anyone with the right prompt can generate it? Is prompting now more valued than the director’s eye, the artist’s genius, the painstaking hours that gave birth to masterpieces? I’m concerned—but I’m also aware that, like it or not, this is the new canvas we’ve been handed. Trying hard with that metaphor - but I am worried. Spoke about this recently with friends and family - will high school students ever write an unassisted essay again? Imagine what people would say - hand written, in class, no assistant. Is it now antiquated to ask for critical thinking? Are we worried about the Orwellian potential that is at hand here?

2. AI at the Bedside—and Under the Skin

Two recent stories caught my eye (thanks to a buddy of mine). First, MIT’s new AI system designed to accelerate clinical research by analyzing patient data at a scale and speed that human researchers can’t match (MIT article). Frankly - we we’re never that fast, but exciting nonetheless. Second, UC Santa Cruz’s breakthrough with smart device bioelectronics, which use AI to help speed wound healing by modulating tissue responses (UCSC article).

Both of these highlight a truth: AI is becoming part of the biology of our work. For patients, this means care that’s not only personalized but responsive in real time. The wound healing device isn’t waiting for the doctor’s next clinic note; it’s adapting minute by minute. The MIT system isn’t waiting for a journal to publish results; it’s accelerating discovery in ways that could shorten the years it usually takes to move from hypothesis to treatment.

The promise is thrilling. But as always, I’m left asking: how do we govern this responsibly? Who interprets the right signal from the noise? And when AI starts making micro-decisions inside our bodies, do we have the right frameworks in place to ensure that “fast” doesn’t come at the expense of “safe”? Today its skin, what about abdominal or thoracic post-op healing acceleration. Thats next.

3. Payers, Process, and a Frosty at Wendy’s

This weekend, I stopped at Wendy’s on a road trip, trying to order food for my kids who were restless and hungry. Their voice AI agent took my order, then mis-typed it, then let me nearly fix it before a human employee jumped in and overrode the system. Suddenly, I found myself annoyed—how dare they interrupt my almost-finished AI editing session? (Also, the cars behind me were adding up…but why was this taking longer than just talking to a person?)

But it hit me: AI is everywhere now. Even the act of ordering fries is “optimized” through algorithms (Wendy’s update). Which brings me to a parallel—health insurers are now leaning heavily on AI to approve or deny care, and Medicare will soon follow (NBC article).

My tension: is ordering food just a transaction, a means to an end? If so, fine. But when we apply the same logic to medicine, the stakes are much higher. Does my insurer see me as a person, or just a claim to be processed? Does my doctor see me as a patient, or as an encounter for billing? These relationships can easily degrade into interactions for monetary gain, stripped of the humanity that makes them sacred.

I still like to believe that the input of my waiter is important, and the relationship can make the dining experience more beautiful.

I can’t get fully behind it—not for my lunch, and certainly not for my healthcare. Because at some point, we have to decide: are these just transactions, or are they relationships worth protecting.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.

In this newsletter, we continue our journey through the fundamentals of AI, its applications in medicine, and its transformative role in faculty development and education. Let’s dive into learning.

Today we have a guest post from a friend and colleague - Dr Travis Bias.

Travis Bias, DO, MPH, FAAFP, is a family medicine physician and Deputy Chief Medical Officer of Health Information Systems at Solventum. He has 17 years of clinical experience across multiple healthcare settings, including current telemedicine practice. Dr. Bias has previously taught courses in Comparative Health Systems and Global Health Diplomacy.

He writes this to help us analyze what is happening in medical training with AI implementation. I hope you enjoy it as much as I did. The more hype that comes our way, the more we should slow down and think about these impacts.

Beyond the Attending’s Experience: AI’s Role in Medical Education

“Has she tried dipping her left hand in warm water for 15 minutes before bed?” My most senior attending physician had the most peculiar tips while precepting, delivered ironically after returning inside from his smoke breaks. These pearls, gleaned from years of deep patient relationships and pattern recognition, were the hallmark of his experience – a testament to the art of Family Medicine that complements evidence-based practice.

Now imagine the same scenario today: a junior resident pulls up a large language model (LLM) tool, swiftly synthesizes the latest evidence for this exact scenario, and presents findings challenging the attending’s decades-old wisdom. The integration yields a more optimal care plan for the patient. The scene, unimaginable even a few years ago, is already reshaping medical education.

Clinical education, starting in the middle of medical school and progressing through residency and fellowship training, has historically been led by attending physicians who have one thing trainees lack: years of experience. Those years include practical exposure to what has worked - and what hasn’t worked - with patients of many types and interventions of many combinations. Those years include reading hundreds of journal articles pointing to the latest in evidence-based care.

While trainees traditionally defer to senior attendings for final decisions, artificial intelligence (AI) is now democratizing and expanding access to medical knowledge. This shift mandates a collaborative approach, particularly vital in Family Medicine, where fluency with evidence retrieval and synthesis can empower junior clinicians to contribute more actively to complex care decisions.

The most junior trainees now have access to the world’s journal articles, applied to the exact patient case in front of them, thanks to AI. These LLM-based tools mean no more sifting through PubMed for medical students. Such solutions also mean that the world’s case reports are at junior trainees’ fingertips. Thousands of journal articles are published each day and these LLMs have consumed every single one of them - an impossible task for even the most brilliant human speed-reader. Access to such solutions will reduce the centrality of any single attending’s accumulated knowledge.

The future of medical education requires using AI-based tools that are explainable and referenceable. And the best medical care will come from the teams that have the highest AI fluency, not necessarily those with the most years of worked or lived experience. This shift does not diminish the value of years of practical clinical experience, but it does change its role. The most effective care teams of the near-future will combine clinical judgment and wisdom with a high level of AI literacy.

The future of health care and professional education requires a concerted effort to ensure physicians and physicians-in-training have a high level of dexterity when it comes to using AI-based tools to ensure their patient population gets the best care possible. Physicians still need training to optimally leverage LLM-based chatbots to strengthen their clinical reasoning.

Adapting to this AI-augmented future, requires preparation at all levels.

First, academic institutions should adopt proposed AI competencies across all levels of education and practice to ensure the “how” and “why” are understood around the scenarios in which AI can be applied to improve outcomes or efficiencies in care delivery. This includes starting the first day of undergraduate medical education and continuing through clinical rotations, residency, and fellowship training. Such curricula should involve understanding the basics around LLMs, AI model training, and the risks of bias, omissions and hallucinations inherent to AI models. Mastery and competency would be judged through testing the use of AI applications in simulated environments, either through quantitative methods and through qualitative feedback from faculty or patients.

The requisite upskilling of attendings will have the same end in mind, but look and feel different given the different starting point in medical knowledge and practical constraints on their time. The North Star for these AI competencies should be those that elevate the patient experience with the health care system and the ones that bring any level of joy back to clinicians’ work.

Next, health care organizations outside of academia should align to similar competencies for their entire medical staff. This ensures that newly minted physicians enter a workforce ready to embrace the latest in AI capabilities. When team members have the same expectations for accessing knowledge, aligning decisions to evidence, and continuously pushing to improve quality, they will inevitably perform better.

Finally, licensing bodies and specialty boards would be wise to accelerate their moves towards continuous recertification that reflects what is known about adult learning and the pace of knowledge creation. If we truly expect adoption of AI in a productive manner, stakeholders to which physicians are accountable for their credentials must reinforce its responsible and explainable usage.

Medical education leaders and accrediting bodies would be wise to jump on the moving freight train and do their best to shepherd the effective and responsible integration of these new technologies. Without their guidance, we will end up with an uneven - perhaps chaotic - application of AI that risks giving rise to a future of widening inequities and preventable care variation. Conversely, a laser focus on embracing AI could lead to a future of better population health through a dramatic increase in access to information - at the right time, in a seamless fashion - for clinicians.

The transformation is already underway; the question is not whether AI will reshape medical education, but whether we will lead that change or be dragged along behind it. The patients we serve are counting on the former.

If you want to reach out to Dr Bias, do so here.

💌 As always, thanks for reading. Get in touch and let me know your thoughts!

Thank you for joining us on this adventure. Stay tuned for more AI insights, best practices, and more future editions of AI+MedEd.

For education and innovation,

Karim

Share this with someone - have them sign up here.