I'm Kevin McDonnell and every week I share the most interesting stories from the world of HealthTech I've been reading so you can find the ideas, people, innovations and technologies that are shaping the future of healthcare.

I read five stories this week.

They were not obviously connected. One was about NHS safety. Another was about the FDA. Two were about private health testing. The last was about an AI scribe rollout.

By the third one, I realised they were all raising the same question.

Who is responsible for what happens after the technology is put into use?

Start with the warning about NHS digital transformation spreading patient harm at scale and speed.

Headlines written like this usually make me suspicious. This one was interesting.

Researchers looked at 14,848 digital technologies across NHS trusts and integrated care boards. 70% had no documented safety assurance. Only 17 per cent were fully assured.

The standards are not new. DCB0129 and DCB0160 have been around for years. They require a formal clinical risk assessment before a tool goes anywhere near a patient.

The figure I kept coming back to was the staffing.

On average, each organisation had one full-time clinical safety officer. In integrated care boards, the figure was 0.4.

Even this makes the situation look better than it is.

In many organisations, clinical safety was another responsibility added to someone’s existing job. Eleven said it sat within a senior leader’s wider role. Twenty-two did not know how much time anyone spent on it.

So the person responsible for clinical safety often has little time to focus on clinical safety.

I have seen the commercial version of this for years.

You give someone another responsibility. You take nothing away. Everyone tells themselves the work is covered.

It is not.

There is simply a name on a form.

This was in my mind when I read the FDA’s discussion paper on generative AI-enabled medical devices.

It came out on 18 August, with comments open until 19 October.

The proposal is roughly to treat these devices as you would treat a doctor going through training. Test their performance. Confirm it clinically. Then keep watching once people start using them.

On paper, this makes sense.

But someone still has to do the testing. Someone has to monitor performance. Someone has to spot when things start going wrong and decide what happens next.

The NHS research gives us some idea of who those people are and how much time they have.

At the same time, more health technology is reaching people before they enter the health system.

WHOOP has added GRAIL’s Galleri test to its Advanced Labs range and removed the membership requirement. Advanced Labs testing starts at around $150. The process involves one blood draw, with access through roughly 2,000 Quest sites across the US and results delivered through the app.

Galleri looks for signals associated with more than fifty cancers.

Neko Health opens in New York on 24 September.

More than 25,000 people have joined the local waiting list, with around 350,000 registered globally. The scan costs $499, takes an hour, and includes a clinician going through the results with you during the same visit.

The company says Americans already spend far more on annual tests and concierge medicine, so it offers a cheaper option.

They are right about the demand.

But the work does not end when someone pays for the test.

A result comes back. Something looks slightly unusual. Someone then has to decide whether it matters.

Often, the next step is an appointment with a GP who did not order the test, has no access to the underlying data, and has ten minutes with the patient.

Nobody has budgeted for this appointment.

This does not mean private testing is a bad idea. It means part of the cost sits somewhere else.

Then I read about Cleveland Clinic.

It felt different because they had treated implementation as a job requiring people, time and money.

Cleveland introduced an ambient AI scribe to 4,000 ambulatory clinicians within four months. After twelve months, more than 4,800 clinicians had used it across over 3.5 million patient encounters.

Sixty per cent said it made them more likely to stay in practice.

Anyone who has seen what clinical documentation does to doctors will understand why this matters.

An ambient scribe is not the same as a cancer test. The risks are different.

The point is what Cleveland put around the product.

They created a group to run the rollout day to day, with smaller groups underneath it. Department chairs received dashboards showing how their teams were using the product. A provider advisory group sent specialty-specific problems back to the product team.

They ran live training three times a day.

Real people staffed the support chat around the clock. They answered more than 900 questions, with an average response time of two minutes.

They also measured use during eligible encounters. They did not count someone opening the product once as adoption, which is where many rollout figures become misleading.

I am not sure governance is the right word for this.

Governance makes me think of committees, policies and quarterly papers.

Cleveland staffed the rollout. They put people next to the technology and left them there for a year.

Every vendor says its product is accurate.

Buyers will start asking different questions.

Who approves it?

Who monitors it?

Who deals with the mistakes?

What happens when the responsible person already has a full-time job?

The FDA is starting to ask for clearer answers. The NHS numbers suggest many health organisations do not have enough people or time to provide them.

Most vendors are still adding features.

All five stories assume a capable person will be there to catch whatever the technology misses. Most plans do not say whose job this is, how much time they have, or who pays for it.

So if you are buying this technology, building it or approving it at board level, ask one question:

When the output is wrong at three in the morning, who owns what happens next, and what work did you remove from their plate so they had time to deal with it?

Welcome back. Kevin McDonnell here. A strange week to read in one sitting. If you only skimmed the headlines, you would think the NHS had finally decided what it thinks about AI: yes, more of it, faster, everywhere. But read the stories next to each other and a more honest picture shows up. We are pressing hard on the accelerator while the things that actually decide whether any of this lasts, trust, security, leadership, the records underneath, all had a wobble in the same seven days.

Start with the money, because that is where the week started. The government put up nearly £30m, with the big slice, £20m, going to roll AI chest X-ray tools out to every trust in England by 2029, and the announcement on GOV.UK is worth reading in full. I like this one more than most AI announcements, and the reason is dull: the tool already exists in half of trusts and has supported over four million patients. This is not a bet on something that might work. It is paying to finish something that does. Pulse covered the same funding from the GP end, and that is the figure that matters to anyone holding a referral, the four-day reporting time against eight for the trickiest cases, and what that does for the 62-day target. Faster reads only help if the rest of the pathway can keep up, mind. A quicker X-ray into the same clogged clinic is just an earlier queue.

On the rules side, the MHRA had a busy time, launching two AI sandboxes more or less back to back, one for medicines safety and one, London Region I, for testing AI medical devices in live NHS settings under supervision, and Fieldfisher have a clear summary of both. I think this is the more interesting development of the week, even if it got less attention than the funding. The earlier AI Airlock was about poking at specific regulatory questions. London Region I is about controlled deployment in real settings. That is a different thing. It is the regulator saying it would rather watch the technology behave in the wild, with guardrails, than wait for a perfect framework that arrives two years too late.

Which brings me to the mood at NHS ConfedExpo, where senior digital leaders basically told the room to stop piloting things. The word doing the rounds was pilotitis, and one national clinical lead called repeated trials of ambient voice tools, well, the printable version is that he thought they were daft, as Medical Device Network reported from the panel. The point underneath the frustration was a good one. The thing that separates a trust that gets value from AI from one that does not is rarely the model. It is leadership, change management, and whether anyone bothered to redesign the work around the tool. The King’s Fund interviewed over 60 leaders and landed on the same unglamorous answer. Conditions, not kit.

There is a contrarian framing of all this that I keep coming back to, and one of the trade write-ups put it well: the £30m is money behind tools already in use, which sits oddly against the rules-first, cautious posture you see everywhere else in health AI. Deploy what works, regulate what is coming. Two speeds running at once.

Now the part that should keep us honest. The Future Health Index for 2026 found that AI is moving through clinical care faster than trust in it is building, among both patients and clinicians, and it flagged shadow AI, staff using tools on the sly that were never signed off, which Medical Daily covered well. That last bit is the one I would not skip past. When the official route is slow, people route around it. You cannot govern what you pretend is not happening.

And then, as if to make the point for us, the Synnovis story grew another limb. The 2024 ransomware attack keeps producing new victims, with Bedfordshire at nearly 33,000 patients from the same attack, which gives you a sense of how the numbers creep once a third party is breached. Here is the link I want to draw, and it is not subtle. We are about to pour a great deal more patient data through a great deal more software, much of it from suppliers. The exposure was contractual, not a hole in the trust’s own walls. Every AI deal we sign is another third party holding something that matters.

The trust gap and the security gap are really the same gap wearing different clothes. People extend trust to systems that are governed, certified, and visibly looked after. They withdraw it the moment data turns up on a dark web forum. You do not get the productivity story without the boring infrastructure story, and we are still much keener on the first.

Two more to round it out. NHS England is handing Microsoft 365 Copilot to around 505,000 staff after a trial suggested it saved 43 minutes a day on admin. Big number. The honest question is whether 43 minutes saved becomes 43 minutes of care, or just 43 more minutes of something else. And a set of sector predictions for the yearmade a point that has stuck with me: a lot of trusts still treat their electronic patient record as a very expensive filing cabinet, somewhere to store notes rather than something to build on. If the EPR is just storage, the AI you bolt on top has nothing solid to stand on. Clean, joined-up data first. The clever stuff after.

Worth a glance beyond our shores too. A roundup of the week in global diagnostic AI shows just how fast the research end is moving, models predicting tumour subtypes off routine slides, that sort of thing. The capability is not the bottleneck. It rarely is anymore.

So where does that leave us. A week where we bought more, regulated more, deployed more, and were reminded, twice, that trust and security are not downstream concerns to tidy up later. They are the thing. I keep wondering whether we have the order back to front, chasing the tools and hoping the foundations catch up.

If you were spending the next pound, would you put it on another clever model, or on the plumbing that decides whether the last one was safe to use? I genuinely do not have a settled answer this week. Have a think and tell me where you land.

Kevin McDonnell, Chairman, CEO Coach, Strategic Advisor.

Code funded by public money, made publicly available. Transparent architecture. Reusable tools that let smaller NHS Trusts and independent developers build on top of existing infrastructure rather than starting from scratch. The COVID contact tracing app was open-sourced and publicly audited during a national crisis - and, by most accounts, it was the better for it.

That era ended on 11 May 2026.

NHS England’s Engineering Board has now mandated that all source code repositories be private by default. Public access is no longer a right. It requires “explicit and exceptional need” and formal sign-off. Hundreds of repos went dark, documentation, architecture diagrams, internal tools, patient-facing apps. All of it.

The trigger was Mythos. Anthropic’s most capable AI model to date, reportedly capable of ingesting large codebases, reasoning across them at scale, and surfacing vulnerabilities that human security teams would never find in a reasonable timeframe. The UK’s AI Safety Institute and NCSC have both, to some degree, validated the threat.

The NHS decided not to wait and find out.

The debate is real - and it’s splitting people who are normally on the same side.

Saif Abed, a respected cybersecurity adviser in NHS circles, called it “a sensible temporary step.” His position: the threat landscape is changing faster than governance frameworks can keep up with, and buying time to think is not the same as getting the answer wrong.

Terence Eden, former head of open technology at NHSX and one of the people who built the open-source culture the NHS is now partially dismantling, argued the opposite. His view: most of this code was already ingested for training purposes years ago. Closing the door now does not lock the horse in. It just makes the NHS less accountable and less collaborative without meaningfully improving security.

Former Health Secretary Matt Hancock signed an open letter calling the move “a huge mistake.” Over 680 people have now signed it, including digital rights advocates and university researchers. One of the researchers had actually used Mythos access to scan NHS repos for vulnerabilities — and found some genuine ones, which they responsibly disclosed to NHS England before this policy was even announced. Their conclusion was not “close everything down.” It was “fix what we found.”

Here is the thing that matters most for anyone building in HealthTech.

This decision is not just about GitHub repositories. It is a leading indicator of something bigger.

For years, the NHS has operated, at least in its stated policy, on the assumption that transparency makes software better and more secure. That if you build with public money, you share the output publicly. That openness is not a vulnerability; it is a feature.

Mythos has cracked that assumption open.

And if NHS England, an organisation with legal, political, and financial incentives to stay open, has decided the calculation has changed, then every HealthTech company with a public API, an open integration layer, or any publicly visible technical architecture should be asking itself the same question right now.

Not “should we close everything?” That is probably the wrong question. But: what does our attack surface look like to a model that can reason across code at the speed and scale of Mythos? What did we assume was low-risk because no human would bother looking? Because a model will bother. And it will do it in minutes.

What this means for HealthTech founders building for the NHS right now:

Security posture is no longer a due diligence checkbox. It is becoming a procurement question. If NHS procurement teams are nervous about AI-assisted vulnerability discovery, they will increasingly ask the companies they buy from how their own security holds up against that kind of scanning.

If your integration architecture, your API documentation, or your technical spec is publicly visible and has never been stress-tested by a frontier AI security model, now would be a reasonable time to think about that.

Not to close everything down. But to understand what you are actually exposing.

Because the NHS just told you the threat is real enough to make them move fast.

Welcome back, Kevin McDonnell here. A lot moved this week. Some of it was loud. Some of it only makes sense when you put it next to two or three other things at once. Let me start where the week started for me.

Doctolib has acquired Medicus - and announced a £100m investment into UK digital primary care, including 150 hires and a new London R&D centre. If you know the GP software market, you know why this matters. EMIS and TPP have owned roughly 99% of that market for the better part of 25 years. Medicus only received NHS validation under the Tech Innovation Framework in June 2025 - it was the first new core clinical system approved in a generation. Doctolib, which already works with more than 40,000 GPs across Europe, has effectively bought its way past the single hardest barrier to entry in British primary care. The CEO framing was predictable (”one team, one ambition”), but the underlying move is not. This is a long-term infrastructure bet on the NHS 10-Year Plan’s shift from hospital to community care. Whether Doctolib can convert European scale into NHS-native product quality is the real question - and that won’t be answered for a few years yet.

Then, running almost in parallel, came the news that NHS England is moving to make all its source code repositories private by default from 11 May, citing concerns about AI models capable of large-scale code ingestion and vulnerability inference. The specific trigger appears to be the emergence of models like Anthropic’s Mythos - which NHSE describes as capable of reasoning across large codebases and identifying exploitable weaknesses. NHSE says the move is temporary while they assess the risk. There are 74 signatories on an open letter asking them to reverse it. The Free Software Foundation Europe has weighed in. Cybersecurity adviser Saif Abed called it “a sensible temporary step” but warned that a transparent, funded mitigation strategy needs to follow. I think that framing is about right. The problem is that “temporary” in NHS technology tends to outlive the conditions that created it. The code was already out on GitHub - closing the doors now does not put that genie back in the bottle, and the FSFE made that point clearly. The more interesting question is whether this signals a broader shift in how NHSE thinks about open infrastructure in an AI-accelerated threat environment.

These two stories - a new entrant buying into primary care software, and NHSE quietly closing its code - sit alongside each other in a way that is worth noticing. The NHS is simultaneously trying to attract more innovation and reduce its exposure surface. Not necessarily a contradiction. But worth watching.

The Royal College of Physicians published a report via NHS Providers this week that I found genuinely useful - not because it says anything dramatically new, but because it puts numbers on things we’ve been talking about in the abstract. Seven in ten physicians support AI in the NHS. Almost exactly the same proportion believe the NHS lacks the digital infrastructure to deploy it effectively. And 79% say they need training on AI tools, while 66% have no access to that training. The part that stuck with me: 69% of 305 physicians surveyed said they are already using personal AI tools to ask clinical questions. That is not shadow IT in the traditional sense - that is clinicians filling a gap that the system has left open. Dr Anne Kinderlerer’s central point is that AI adoption is a leadership, governance and regulatory challenge before it is a technology challenge. I’d add: it is also a data quality challenge. The report flags that many EPRs are so poorly standardised - even within the same supplier - that the datasets needed to train and deploy AI are incomplete and prone to bias before anyone has written a single line of model code.

Sitting alongside this, techUK published its AI-enabled healthcare sprint report - a six-month campaign running through to May 2026 covering how the UK can lead on development, application and commercialisation of AI in health. The report is member-facing and the full detail is behind a login, but the public framing aligns with something I keep coming back to: the UK has genuine strengths in clinical AI research and regulatory science, but converting those strengths into consistent NHS deployment at scale remains a problem that better policy and infrastructure hasn’t solved yet. The 10-Year Plan target of making the NHS “the most AI-enabled healthcare system in the world” is an aspiration that will require considerably more than aspiration to deliver.

The MHRA announced a £3.6m funding uplift for its AI Airlock programme - £1.2m per year across 2026-2029 - securing multi-year continuity for the UK’s only regulatory sandbox for AI as a Medical Device. Phase two has covered large language models, voice tools, and diagnostics for cancer and rare diseases. The findings from phase two are due in summer 2026. What matters here is not the funding amount, which is modest in absolute terms, but the move from annual to multi-year cycles. That shift allows longer-horizon testing models - which is exactly what’s needed when you’re trying to understand how an AI diagnostic performs over 18 months of real-world use, not just in a six-week sandbox sprint. TORTUS AI’s CEO Dom Pimenta described the cross-educational aspect - regulators and companies genuinely learning from each other - as the programme’s most underrated feature. I think he is probably right about that.

NHS England has sought market engagement on end-to-end diagnostic testing pathways for NHS Online, the virtual hospital service. The notice is looking at digital test ordering, appointment booking, and digital notification of results - the full cycle, not just one step of it. The contract value is modest at £160,000, running from December 2026 to December 2030, and the engagement deadline is 22 May. But the intent is worth noting. NHS Online is central to the 10-Year Plan’s ambition to connect patients to expert clinicians digitally by 2027. Getting diagnostics embedded into that pathway - rather than bolted on as an afterthought - is technically and organisationally harder than the notice makes it sound. Market engagement at this stage is the right move, but suppliers should read it as a signal rather than a guaranteed opportunity.

Relatedly, NHS England’s Path to Live environment update for the week of 4 May flagged work underway on a new Advice and Guidance (A&G) service for e-RS, with the INT training environment in use by the development team through 19 May. More relevant to the diagnostics story than it appears at first read - A&G within e-RS is one of the mechanisms by which GPs can get specialist input without a full referral, and expanding it sits squarely within the diagnostics-access ambition NHSE is trying to build. The plumbing matters even when it isn’t interesting.

On the operational side, Deep Medical’s AI appointment prediction tool is expanding to ten NHS trusts following a successful six-month pilot at Mid and South Essex. The software uses algorithms and anonymised data - weather, traffic, employment patterns - to predict likely non-attendances and offer patients more convenient slots, including evenings and weekends. The pilot prevented 377 DNAs, saw an additional 1,910 patients and is estimated to save the trust £27.5m a year. Missed appointments cost the NHS roughly £1.2bn annually. I’ll be honest - these numbers always come with caveats about how they’re calculated. But even at a fraction of that figure, there is clearly something here worth scaling. The more substantive question is whether this kind of localised operational AI gets absorbed into a coherent national strategy or stays as a patchwork of trust-by-trust deployments that never quite add up to system-level change.

And then there is the GP data story, which has been running quietly under everything else this week. Pulse Today reports that ICBs are increasing pressure on GP practices to boost patient records access via the NHS App - with NHS England noting 18.6 million record views last month, up from a 9.6 million average last year. At the same time, GPC has entered collective action on data sharing agreements, with practices being asked to cease signing new voluntary data-sharing arrangements that extract patient data for secondary uses, including research. The BMA’s position is that GPs remain data controllers and have not been given adequate clarity on what data flows they are actually party to. NHSE’s position is that contractual obligations exist and must be met. These two things are not yet reconcilable, and the data infrastructure the NHS needs to make AI work at scale sits right in the middle of this dispute. That is not a comfortable place for it to be.

This has been a week where the distance between the ambition and the foundations was visible in almost every story. £100m into primary care software. A regulatory sandbox getting multi-year funding. Clinicians already using ChatGPT because there’s nothing approved to use instead. Code being closed because AI has made openness feel dangerous. None of it is tidy. Probably shouldn’t be.

Kevin McDonnell - Helping HealthTech businesses unlock potential, accelerate growth and successfully scale.

Hi All,

I'm Kevin McDonnell and every week I share my round-up of the most interesting stories from the world of HealthTech I've been reading so you can find the ideas, people, innovations and technologies that are shaping the future of healthcare.

Striking the Balance Between Innovation and Responsibility with AI in Healthcare - The rapid integration of AI into healthcare systems is reshaping patient care and operational efficiencies. Yet, it poses ethical dilemmas around transparency and accountability. Policymakers are tasked with balancing the benefits of AI-driven diagnostics against risks like algorithmic bias and data misuse. Collaboration between clinicians, technologists, and ethicists is critical for setting guardrails and achieving responsible AI deployment. AI offers immense potential, but trust gaps and unclear regulations create barriers. The solution lies in co-developing policies that align technological innovation with ethical practice, ensuring safety and equity.

Dictate IT Launches AI-Powered Clinical Assistant HM24 - HM24, an AI-driven tool, promises to reduce clinician workload by automating administrative tasks like transcription and documentation. This allows healthcare providers to dedicate more time to patient care. Early adoption in hospitals reveals faster record completion and reduced burnout rates among medical staff. However, concerns about data accuracy and long-term reliance on such tools remain under scrutiny. Tools like HM24 mark a shift toward tech-assisted caregiving. Yet, rigorous testing and training for healthcare workers are crucial to mitigate unintended errors from automation.

Revolutionised NHS App at Heart of Plan to Cut Waiting Lists - The updated NHS app now integrates advanced features such as predictive analytics for patient scheduling and triaging. This transformation aims to tackle backlog challenges and reduce waiting times. Patients gain access to real-time updates, while clinicians benefit from streamlined workflows. While initial feedback has been positive, critics worry about digital literacy gaps and access issues in underserved populations. Bridging digital divides is key to ensuring innovations like this serve all patients, not just those already digitally savvy.

Health Tech Leaders Set Out Their Digital Priorities for 2025 - Leaders emphasize scaling interoperability, enhancing cybersecurity, and integrating AI into routine care by 2025. Discussions highlight the need for cross-sector collaboration to unify fragmented systems. Success depends on investments in workforce training, regulatory clarity, and addressing privacy concerns as technology adoption accelerates. Interoperability has been a buzzword for years, but tangible progress remains elusive. Stronger incentives and global benchmarks could accelerate alignment across healthcare ecosystems.

NHS England Hunts for New CIO - As NHS England seeks a new Chief Information Officer, the focus is on finding a leader capable of driving the digital transformation agenda. This role demands expertise in overseeing large-scale IT projects, aligning them with national healthcare goals, and managing the cultural shift required for innovation acceptance. The CIO role will be pivotal for healthcare modernization. Selecting someone with both tech vision and people skills will be essential to navigate this complex landscape.

Year in Review and Outlook: Global Healthcare Private Equity Report 2025 - Private equity in healthcare surged in 2024, driven by interest in outpatient care, med-tech, and digital health. Investors focus on scalable solutions addressing demographic changes and chronic disease management. However, geopolitical uncertainty and rising costs are projected to challenge deal-making in 2025. Private equity’s role in reshaping healthcare access is undeniable. Focusing on long-term value creation over short-term returns could ensure sustained impact.

AI Drives Healthcare VC Investment to $23 Billion in 2024, SVB Report Shows - Venture capital investments in healthcare AI hit a record $23 billion in 2024. Startups focusing on predictive analytics, precision medicine, and workflow automation dominated funding. The pace of innovation intensifies, but early-stage ventures face pressure to demonstrate scalability and profitability quickly. VC backing fuels groundbreaking innovation, but the rush to commercialize can stifle creativity. Balancing speed with sustainable growth strategies is critical.

FDA Issues Draft Guidance on AI-Enabled Medical Devices - The FDA’s draft guidance on AI-powered medical devices outlines requirements for algorithm transparency, adaptive learning models, and safety standards. This framework addresses industry demands for clarity on regulatory expectations. Stakeholders are invited to provide feedback to shape final guidelines. These guidelines are overdue. They provide much-needed structure, but balancing innovation flexibility with patient safety will require careful calibration.

Healthcare Execs at CES Share Predictions for the Next Decade - Executives predict that AI, personalised medicine, and integrated care platforms will dominate healthcare advancements over the next decade. While the promise of tech-enabled care excites, concerns linger over data privacy, workforce readiness, and cost-effectiveness. The future feels within reach, but without equitable access and robust governance, these predictions risk leaving vulnerable groups behind.

Healthcare & Life Sciences Leaders Expect More Mergers in 2025 - Consolidation in healthcare and life sciences is anticipated to rise, driven by cost pressures and the need for scale. However, integration challenges, including culture clashes and operational inefficiencies, pose risks to achieving intended synergies. Mergers may solve financial woes short-term, but sustainable impact will require aligning merged entities’ missions and ensuring employee buy-in.

Last week, I had the privilege of chatting with Ruben Hassid, a visionary thinker and entrepreneur whose focus aligns perfectly with a challenge many HealthTech CEOs and Founders face.

Creating effective LinkedIn content.

For leaders immersed in driving innovation, transforming healthcare delivery, and scaling solutions that save lives, crafting impactful LinkedIn posts often feels like an afterthought - or an insurmountable task.

Yet, LinkedIn offers a unique opportunity for HealthTech leaders to influence, inspire, and inform. But how do you translate technical expertise, clinical insights, or groundbreaking research into posts that resonate with a diverse, non-technical audience?

Enter Ruben’s product, Easygen. Ruben generously created a video (scroll down) demonstration of his product, showcasing how it’s designed to simplify content creation. Watching the demonstration, I was struck by the simplicity and power of the tool. Easygen doesn't just churn out generic posts - it helps leaders craft tailored messages that connect deeply with their target audience, whether that’s potential investors, healthcare practitioners, or fellow innovators.

Many HealthTech leaders find themselves stuck in a paradox. They are steeped in groundbreaking insights and pioneering ideas but struggle to translate these into accessible, engaging narratives. The result? Missed opportunities to share a vision, influence key stakeholders, and build credibility in an industry where trust and thought leadership are paramount.

The problem isn’t a lack of stories - it’s the sheer complexity of the stories HealthTech leaders have to tell. These narratives often include intricate details about clinical validation, regulatory compliance, or technological breakthroughs. While fascinating to industry insiders, this level of detail can alienate broader audiences.

The challenge lies in simplifying without oversimplifying - making the complex compelling while maintaining integrity.

Imagine this. Instead of staring at a blank screen, wondering how to explain a new AI-driven diagnostic tool to a mixed audience of investors and clinicians, a HealthTech CEO can leverage Easygen to frame their message.

For HealthTech CEOs, being visible on platforms like LinkedIn isn’t just about personal branding or self-promotion, it’s a responsibility. Healthcare is an industry that thrives on trust, transparency, and collaboration. When leaders share their insights, they aren’t just building their brand, they’re shaping industry conversations, inspiring innovation, and creating partnerships that drive real-world change.

Consider the ripple effects of a single impactful post. A CEO who shares their journey in scaling a telehealth platform could inspire other startups to pursue similar paths. A founder discussing lessons learned from a clinical trial failure might save others from making the same mistakes. A leader showcasing their company’s commitment to ethical AI use in diagnostics could set a standard that reshapes the entire field.

One of my key takeaways from my conversation with Ruben is that tools like Easygen are enablers, not replacements. They don’t take away the authenticity of a leader’s voice, they enhance it. By removing the friction of starting from scratch and offering intelligent prompts, tools like Easygen free up HealthTech leaders to focus on what they do best, innovating, leading, and making an impact.

As I reflect on my conversation with Ruben, I can’t help but wonder about the broader challenges leaders face in creating content. Are you finding it difficult to translate your expertise into posts that resonate? Have you discovered tools or frameworks that make the process easier?

What’s holding you back (or propelling you forward) in the content creation journey?

And thank you Ruben Hassid for taking the time to put this video together 🙏

Here is the Easygen website - easygen.io

Hi All,

The NHS finds itself under immense pressure, grappling with a “quad-demic” as flu hospitalisations surge by 350%. This underscores the fragility of healthcare systems under strain. Simultaneously, the NHS is making strides with cutting-edge treatments like the new eye cancer therapy that patients have described as life-changing. It's a juxtaposition of the difficulties the NHS faces, despite record funding, as highlighted in a critique of the system’s inefficiencies. These stories left me pondering whether systemic challenges dilute the impact of even the most groundbreaking advances.

Elsewhere, innovation is steering healthcare into the future. Estonia’s digital health revolution has set an impressive benchmark, demonstrating how tech can bridge gaps in care. In the UK, initiatives like a £1m tech fund to address health inequalities are promising steps toward more equitable care. Meanwhile, Northern Ireland’s heart failure home serviceexemplifies how small, targeted programs can make meaningful differences for patients.

Artificial intelligence continues to reshape the landscape, with Google showcasing its healthcare AI, which hints at a future of precision medicine. However, as GE Healthcare’s AI solutions remind us, adopting AI isn’t just about efficiency—it’s about supporting overburdened healthcare professionals. These innovations spark optimism but also raise questions about preparedness to handle ethical and regulatory implications.

One story that stood out involved a data-driven approach to mental health. The idea of leveraging data to create personalised, predictive care is compelling, especially given the pressing need for accessible mental health services. It connects well with a broader trend of fostering partnerships, such as Ireland’s efforts to support SMEs in healthcareinnovation. Collaboration appears to be the thread binding many of these breakthroughs.

On that note, you have the opportunity to contribute to shaping the future of UK HealthTech. As part of the MARRS (MedTech Accelerator Rapid Regulatory Support) program, the UK Government and CPI are addressing investment challenges in the HealthTech sector. You can participate in this initiative by:

• MARRS Interviews: Share your insights during a 30–45-minute virtual interview on topics such as global opportunities, funding requirements, and best practices. Your contributions will directly influence recommendations for the government and stakeholders. Contact Thea Tebble for more details.

• MARRS Roundtables: Join an in-person roundtable in cities like Birmingham, Edinburgh, Leeds, Belfast, Cardiff, or London this January and February. These events offer discussions, networking opportunities, and a collaborative lunch - https://lu.ma/calendar/cal-o4GlO23VB4UExxF

Reflecting on all this, I’m struck by a recurring theme. The balance between ambition and reality. For every leap forward in technology or care delivery, there’s a counterweight in the form of systemic hurdles, funding gaps, or ethical dilemmas. It’s a reminder that progress, while necessary, must be measured against its practical impact on patients and providers alike.

What do you think? Are we moving too fast, too slow, or just right when it comes to integrating technology into healthcare? And how do we ensure that advancements don’t widen existing inequalities but instead address them head-on? I’d love to hear your thoughts.

Hi, I’m Kevin McDonnell, and I’ve spent over 25 years building, scaling, and exiting companies, with a particular focus on HealthTech. Today, I'm a coach and advisor to ambitious HealthTech CEOs, helping them navigate the complexities of growth, unlock potential, and accelerate their success.

Over the years, I’ve seen a recurring problem. Too many founders and investors treat HealthTech as just another SaaS business. Only to discover that healthcare operates under entirely different rules. This article comes from a deep understanding of what works (and what doesn’t) - it's not meant to be exhaustive but hopefully prompt introspection.

I hope it's helpful.

1. Healthcare buyers involve multiple decision-makers, including clinicians, administrators, and procurement teams. This makes the sales process significantly more complex and time-consuming.

2. Every new market introduces unique compliance requirements (e.g., FDA, GDPR, HIPAA, MHRA). Meeting these standards requires detailed documentation, clinical trials, and third-party peer review.

3. Healthcare providers need to see validated outcomes, not just product features. Trust must be earned through clinical studies, peer-reviewed evidence, and partnerships with respected institutions.

4. Unlike SaaS, where bugs can be patched quickly, HealthTech solutions impact real lives. A software glitch in a healthcare setting can lead to life-threatening consequences, creating an extremely low tolerance for mistakes.

5. You’re not just selling to one SaaS buyer - your solution must satisfy patients, clinicians, administrators, and regulators, each with different priorities and concerns.

6. Healthcare systems tend to be conservative and risk-averse, often relying on legacy systems. Convincing them to adopt new solutions requires significant effort and proof of ROI.

7. Healthcare organisations often require tailored integrations with existing systems, which adds complexity and extends onboarding timelines (professional services which doesn't fit into the SaaS playbook).

8. Managing sensitive patient data means HealthTech companies must meet strict security standards like HIPAA, Cyber, DTAC, DCBs and ISO certifications, adding extra layers of effort and scrutiny.

9. Many healthcare organisations operate on constrained budgets, making price sensitivity a significant barrier to entry.

10. HealthTech solutions are heavily influenced by changing healthcare policies and funding models, which can unpredictably affect market conditions and growth opportunities.

11. Unlike SaaS, where value can be perceived through efficiency or time saved, HealthTech buyers demand metrics for improved patient outcomes, realisable cost reductions, or operational efficiencies.

12. Scaling HealthTech involves maintaining compliance, updating software in alignment with evolving regulations, and continuous user training - all while proving clinical efficacy.

13. Unlike SaaS the healthcare industry varies widely by geography, with different regulations, patient behaviours, and purchasing models. Scaling globally requires significant localisation efforts.

14. HealthTech products often struggle to integrate into a fragmented ecosystem of existing platforms and devices, requiring significant engineering resources.

You need to scale your HealthTech company like a HealthTech company - not like a SaaS company.

On the other hand, the news of NHS employee records being exposed underscores a critical vulnerability. This breach serves as a stark lesson about the pressing need to secure the very systems that form the backbone of healthcare services. As organizations push for digital transformation, balancing innovation with robust cybersecurity becomes even more essential.

Progress isn’t limited to tackling vulnerabilities. Initiatives like Digital Health and Care Wales's new partnership and NHSNorth Yorkshire’s £40M digital and IT investment reflect an ongoing commitment to reimagine healthcare delivery. These moves highlight how collaboration and targeted investments can unlock new possibilities for more efficient, patient-centered care.

One particularly striking example of innovation comes from the NHS’s adoption of drones for medical supply delivery. By combining speed, efficiency, and environmental sustainability, this approach hints at how technology could reshape logistical challenges, especially in remote areas. It also ties into broader conversations about adopting greener healthcare practices, as emphasized in the World Economic Forum’s insights on AI. While AI’s role in diagnostics and personalized care is rapidly expanding, the challenges surrounding ethical considerations and governance remain pivotal.

Another perspective worth noting comes from the Nordic model of post-pandemic healthcare. Their focus on virtual wards and enhanced digital feedback systems showcases how patient involvement can be amplified through technology. Similarly, efforts like the NIHR’s global initiatives demonstrate the importance of building interconnected systems that transcend local boundaries, fostering global collaboration in healthcare.

In reflecting on the financial aspects of these changes, the reexamination of digital health ROI is particularly compelling. Evaluating success should go beyond immediate cost savings to include long-term health outcomes and equity. This shift in perspective could help ensure that digital innovations benefit the widest range of patients.

Finally, a Nature study on AI in health diagnostics underscores the impressive strides these systems have made while urging caution. As healthcare becomes increasingly digital, balancing machine precision with human expertise will be critical to achieving both reliability and trust.

What stands out across these stories is the interplay between opportunity and responsibility. As healthcare embraces digital tools and transformative models, the challenge lies in ensuring that these advancements are secure, inclusive, and aligned with patient needs. It’s a fascinating journey to follow—and one that invites reflection on how these shifts might shape not just the future of care but also how we define success in this space.

I’d love to hear your thoughts - how do you see these changes influencing the way we experience healthcare?

Hi, Kevin McDonnell here. Thank you for reading my latest newsletter. Every week I share my round-up of the most interesting stories from the world of Digital Health I've been reading so you can find the ideas, people, innovations and technologies that are shaping the future of healthcare. I hope you enjoy them! Happy reading! Don't forget to follow me on LinkedIn for more daily insights.

Generative AI Transforms Healthcare Administration

The use of generative AI in healthcare administration promises to overhaul repetitive, manual tasks, improving operational efficiency. Automating patient intake processes, data extraction, and administrative workflows reduces human error and frees up healthcare workers for more complex tasks. Despite the excitement, challenges like data privacy and security require attention. Healthcare systems stand to gain significantly from these innovations but must navigate implementation hurdles.

AI-Powered Skin Cancer Diagnosis: A Game of Probability

Artificial intelligence may change skin cancer diagnosis, but reliance on probability poses risks. Dermatologists can diagnose melanoma with greater accuracy, but the margin for error increases when AI alone handles cases. The technology's promise is tempered by real concerns around misdiagnosis, which could lead to catastrophic consequences if improperly handled. Vigilance and human oversight remain essential to integrate AI safely into clinical practice.

FDA Strengthens AI Regulation to Ensure Patient Safety

The FDA is tightening regulations around AI-based medical devices, intending to strike a balance between innovation and patient safety. With algorithms being used to detect diseases, set treatment plans, and monitor patient conditions, the margin for error must be reduced. Strict oversight ensures these tools meet rigorous standards while driving innovation. Striking the balance remains critical to realising AI’s full potential in medicine.

AI in Breast Cancer Detection: Promises and Pitfalls

AI’s potential in breast cancer detection is promising yet filled with complexity. Physicians and radiologists welcome these tools as they improve early-stage detection, but over-reliance on AI could lead to false positives and unnecessary treatments. The real issue lies in blending machine insights with human expertise to avoid mistakes that carry profound patient impacts. Continued collaboration between AI developers and clinicians is essential.

Five Aspects of Artificial Intelligence and Healthcare for 2024

AI's footprint in healthcare will expand across multiple dimensions in 2024, from clinical decision support systems to virtual nursing assistants. These technologies can potentially revolutionise healthcare, but only if ethical frameworks and proper governance are in place. AI tools should be leveraged for efficiency and improving the quality of care, thus requiring an interdisciplinary approach to mitigate risks.

Ambient AI Brings Real-Time Automation to Healthcare

Ambient AI is ushering in a new era of real-time automation within healthcare settings. From monitoring vital signs to facilitating patient check-ins, this AI works in the background to reduce administrative burdens. It minimises disruption in clinical workflows while enhancing patient experience. However, as more devices become interconnected, cybersecurity concerns must be addressed head-on to protect patient data from breaches.

NHS AI Predictive Tool to Identify At-Risk Pregnancies

The NHS has introduced AI-powered predictive tools to help identify high-risk pregnancies earlier. These tools analyse a wide range of patient data, offering healthcare professionals critical insights to improve maternal outcomes. The technology may dramatically reduce complications if implemented effectively, though it faces the hurdle of training staff and maintaining data accuracy in real-world settings. The benefits outweigh the risks in this field.

Healthcare’s Push into Tier II Cities: The Next Frontier

Healthcare investments in Tier II cities are rapidly rising, driven by increased demand for accessible care. These regions offer significant growth potential as urban healthcare providers expand their reach. Private players and startups are setting up hospitals, diagnostic centres, and telemedicine networks, helping bridge the healthcare gap. The move could alleviate pressure on metro facilities while providing underserved populations with quality care.

AI Enhances Genomic Data Analysis in Healthcare

AI’s ability to accelerate genomic data analysis is game-changing. Researchers can now sift through vast datasets to identify genetic markers for disease at speeds previously unimaginable. This could lead to breakthroughs in personalised medicine and the treatment of rare diseases. However, data privacy remains a looming concern, and the healthcare system will need to set boundaries for how this sensitive information is used.

Google Licenses AI Model for Diabetic Retinopathy in India and Thailand

Google’s AI model for detecting diabetic retinopathy is now licensed to partners in India and Thailand. By screening patients early, this technology could prevent blindness in thousands of cases. The rollout demonstrates AI's tangible benefits in healthcare, particularly in low-resource settings. Yet, concerns remain around scalability, as local healthcare workers must be trained adequately to implement these solutions without errors.

Answer Digital shortlisted for prestigious UK Employee Ownership Awards

Answer Digital, a Leeds-based digital consultancy that works closely with local and national NHS teams, has been shortlisted for the prestigious ‘Employee Owned (EO) Business of the Year’. The awards celebrate the achievements of businesses that truly deliver people power and recognise businesses and individuals who drive trademark employee ownership practices such as enhanced employee wellbeing, exceptional business performance, and people-powered growth.

Join The Future Health community today! A community of HealthTech leaders finding the ideas, people, innovations and technologies shaping healthcare. Every Monday, an article exploring the future of healthcare and every Friday, a roundup of the week's news and developments. Whenever you're ready, share your news, write a guest article, sponsor the newsletter.

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There is no doubt that AI is reshaping every facet of our world, and healthcare is no exception. From diagnostics to personalised treatment plans, AI has the potential to change how we approach medical care. But for every story of breakthroughs in accessibility, there are concerns about bias, equity, and inclusivity. Is AI genuinely democratising healthcare or reinforcing harmful biases embedded in flawed datasets?

It's an interesting question, isn't it?

While AI promises greater efficiency and accessibility in healthcare, it also harbours the dangerous potential to amplify biases. In the race to innovate, we might create systems that discriminate without realising it.

AI's Role in Making Healthcare More Accessible

AI technology is being hailed as a game-changer for accessibility in healthcare. From predictive algorithms to AI-powered imaging, the ability to instantly process massive amounts of data has opened up new frontiers in medical care. For instance, AI can:

• Transform rural healthcare: AI-driven telemedicine platforms allow patients in underserved areas to receive specialist care without travelling.

• Personalized treatments: AI can analyse genetic data to suggest individualised treatments for a patient’s unique biological makeup.

• Faster diagnostics: AI can process lab results and medical images at unprecedented speeds, identifying abnormalities faster than traditional methods, potentially saving lives.

This accessibility isn’t limited to the affluent few but has the potential to reach the underserved (if implemented ethically). The impact on traditionally marginalised communities could be incredible.

Are AI Systems Reinforcing Biases?

However, the other side of this innovation is the genuine risk of bias. AI systems are only as good as the data they are trained on, and that’s where problems arise. If the datasets feeding AI are biased - whether in terms of race, gender, or socioeconomic status - the outputs will be biased too. Here are a few alarming cases that showcase the darker side of AI in healthcare:

• Facial recognition systems: Some AI facial recognition tools used in diagnostics have proven significantly less accurate for people with darker skin tones. Imagine the potential consequences in life-or-death healthcare scenarios.

• Biased data in diagnostics: A study by the American Medical Association found that AI algorithms used in emergency rooms were more likely to misdiagnose women and people of colour because the data they were trained on disproportionately represented white males.

• Access to care: AI systems meant to streamline access to care may inadvertently prioritise wealthier, urban patients if the training data skews toward this demographic.

This is infuriating for those working toward equity in healthcare. The promise of AI should be to eliminate such disparities. Not perpetuate them!

Tackling the Problem in Your Own Business

The AI systems you deploy in your own businesses - whether for recruitment, customer service, or decision-making - are likely facing similar challenges. Just as biased datasets in healthcare can result in inequitable care, biased datasets in business can lead to discriminatory hiring practices, flawed product recommendations, or even unequal customer service.

To ensure your AI systems are ethical, ask these critical questions:

• Where does your data come from? You may unintentionally perpetuate biases if it doesn't represent the full spectrum of human diversity.

• Who is building your AI? Diverse teams create more inclusive AI systems. Ensure that people of varied backgrounds are involved in development.

• How are you auditing outcomes? Regularly evaluate whether your AI systems produce biased or unequal results.

Timnit Gebru, one of the foremost experts in AI ethics, argues that we must address biases at the dataset level to prevent AI from reinforcing existing disparities. The World Health Organization (WHO) has also warned about AI's ethical implications in healthcare, urging global leaders to focus on creating systems that prioritise inclusivity.

Studies from MIT, Harvard, and other academic giants have corroborated these concerns, showing that AI's power to do good is closely tied to the ethics of its design. Over 80% of AI researchers agree that biases in data pose the most significant threat to the responsible deployment of these technologies.

So, how can we turn the tide and ensure that AI in healthcare - and beyond - benefits everyone equally? Here are some actionable steps:

1. Audit AI systems for bias regularly: Auditing your AI models is essential in healthcare or your business. Use third-party evaluations to avoid confirmation bias.

2. Promote transparency: Insist on explainable AI. Patients, customers, and employees have the right to understand how decisions affect them.

3. Diversify your data: The more inclusive your datasets, the better your AI systems will serve everyone. If you're collecting data, ensure it’s representative of all demographics.

4. Support policy changes: Advocate for legislation that enforces ethical standards in AI development, ensuring that these tools benefit society.

Can We Ever Eliminate Bias in AI?

Some argue that eliminating bias in AI is impossible because human bias will always find its way into the systems we create. However, others counter that bias can’t be entirely eradicated, but it can be significantly mitigated through rigorous testing, diverse data, and transparent methodologies. What’s your opinion? Is the goal to eliminate bias entirely or simply to manage it effectively?

AI is not going away, and its impact on healthcare will only grow. As a healthcare technology leader, it’s your responsibility to ensure that the tools you develop, fund, or implement are inclusive, ethical, and just.

Hi, Kevin McDonnell here. Thank you for reading my latest newsletter. Every week I share my round-up of the most interesting stories from the world of Digital Health I've been reading so you can find the ideas, people, innovations and technologies that are shaping the future of healthcare. I hope you enjoy them! Happy reading! Don't forget to follow me on LinkedIn for more daily insights.

AI Triage System Achieves 73% Reduction in Waiting Times, According to NHS Study

A recent NHS study highlights an AI triage system that successfully slashed waiting times by 73%. The AI platform handles initial patient assessments and significantly shortens the process by automating steps that would traditionally require human intervention. Despite its efficiency, there are growing concerns about over-reliance on automated systems and the potential for errors without proper oversight. AI-driven solutions, while promising, need constant monitoring to ensure ethical and safe patient care.

It’s Time to Create Accountability for Improving Healthcare Communication

This article calls for serious accountability in healthcare communication, a notorious bottleneck. It suggests that better communication between professionals and patients would drastically improve outcomes. From miscommunication during patient handovers to overly complex jargon, it's no surprise that inefficiencies creep in. The article argues that without holding stakeholders accountable, meaningful improvement will always elude us. Communication, after all, isn’t optional; it’s vital to patient care.

92% of US Healthcare Organizations Experienced a Cyberattack in the Past Year

With 92% of US healthcare organizations falling victim to cyberattacks, security is no longer a secondary concern. Breaches have caused serious financial and reputational damage. The article underscores that cybercriminals target healthcare due to its invaluable patient data, yet too many organizations remain ill-prepared. The situation is exacerbated by outdated systems, insufficient cybersecurity training, and over-reliance on third-party vendors. As digital health evolves, so must our defenses.

AI’s Potential to Improve Healthcare Access Could Be Offset by Exploitation

AI holds great promise for enhancing healthcare access, particularly in underserved regions. But there’s a dark side. AI models could be manipulated or exploited, skewing results in favor of certain demographics while ignoring others. The article highlights how the underrepresentation of minorities in AI training datasets already leads to biased outcomes. Ensuring equity in AI requires stringent oversight and transparency in its development.

Online GP Consultations Reached Peak in August 2024

The demand for online GP consultations peaked in August 2024, driven by convenience and continued concerns about in-person visits. However, the report warns that this surge is testing the limits of virtual healthcare services, with some patients experiencing longer waits or reduced quality of care. While digital consultations have undeniably expanded access, they may also be stretching resources too thin, creating a trade-off between volume and quality.

MaraBio Raises $15M for Its Blood Test That Detects Autism and More

MaraBio raised $15 million to advance its groundbreaking blood test that detects autism and other neurodevelopmental conditions. The blood test offers a non-invasive alternative to traditional diagnosis methods, aiming to improve early detection rates. This funding round will allow MaraBio to refine the technology and potentially revolutionize how conditions like autism are diagnosed. However, the test still faces hurdles in gaining regulatory approval and mainstream adoption.

Discover How Healthcare Leaders Are Using Generative AI

Generative AI is redefining the role of healthcare leaders, helping them analyze vast amounts of data to uncover insights, predict patient outcomes, and optimize operations. The technology is already streamlining administrative tasks, freeing up more time for patient care. Yet, leaders must balance excitement with caution, as the risks of bias in AI models and potential misuse could undermine these benefits. The key lies in responsible implementation.

The Rise of Physical AI: When Intelligent Machines Meet the Real World

Physical AI is set to revolutionize industries by merging artificial intelligence with physical machines. The article discusses how robots and AI-powered devices are being designed to perform tasks that require physical interaction with the environment, from assisting in surgeries to elderly care. Yet, as AI integrates more into physical spaces, concerns about privacy, job displacement, and ethical regulations loom larger. The balance between progress and disruption is delicate.

Investor Duo Launch New HealthTech VC Firm

A new health tech venture capital firm was launched by two seasoned investors to fund startups aiming to revolutionize healthcare. Their focus is on early-stage companies with transformative technologies in diagnostics, telemedicine, and wearable tech. The article highlights their approach to scouting for talent and fostering innovation, though the challenge remains in balancing rapid growth with sustainable healthcare solutions. VC firms will play a pivotal role in healthcare's future.

Health Note Releases ‘State of Clinical Burnout’ Report; Findings Reveal Overdependence on EHR AmongOther Tech-Induced Barriers

A recent report from Health Note highlights alarming levels of clinical burnout, exacerbated by over-reliance on electronic health records (EHR). The excessive administrative burden placed on healthcare professionals has led to increased dissatisfaction and fatigue, raising concerns about long-term sustainability. While EHRs were designed to streamline processes, their current implementation appears to be more of a hindrance than a help. Finding a balance between tech efficiency and human well-being is critical.

Join The Future Health community today! A community of HealthTech leaders finding the ideas, people, innovations and technologies shaping healthcare. Every Monday, an article exploring the future of healthcare and every Friday, a roundup of the week's news and developments. Whenever you're ready, share your news, write a guest article, sponsor the newsletter.

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A recent study in The European Journal of Clinical Nutrition warns that while AI technology offers tremendous potential, it could pose significant risks to patient health, especially when managing critical conditions like diabetes and metabolic abnormalities.

Whilst AI is increasingly relied upon in many industries, healthcare professionals must stay alert to the potential dangers of depending solely on these tools. This study emphasises that AI can support personalised education but lacks the precision required for safe medical guidance.

The Rising Role of AI in Healthcare

There is no doubt that AI is revolutionising various sectors, and healthcare is no exception. AI platforms, like OpenAI’s ChatGPT, are being integrated into medical applications to offer insights, advice, and real-time feedback. These tools, designed to assist healthcare professionals, promise to streamline workflows and enhance patient care.

For example, healthcare providers might use AI chatbots to suggest meal plans, manage chronic conditions, or answer patients' frequently asked questions.

The Alarming Findings from the Latest Study

A recent study by a team of researchers led by Professor Farah Naja from the University of Sharjah has raised red flags about AI’s role in offering medical advice. The research analysed ChatGPT’s performance in managing diseases such as Type 2 Diabetes and Metabolic Syndrome. What they found was unsettling: ChatGPT missed critical recommendations, such as weight loss and proper nutrient guidance, essential for effective disease management.

The team fed 63 prompts into ChatGPT’s system, covering three major domains: dietary management, nutrition care processes, and menu planning. Unfortunately, the chatbot’s advice was often incomplete or outright incorrect, lacking specific guidance that aligns with the American Diabetes Association and other medical guidelines.

Why These Findings Matter to Healthcare Providers

Accurate and complete information is crucial for healthcare professionals, particularly those managing chronic conditions like diabetes. Weight loss, balanced diets, and lifestyle changes are critical to managing metabolic conditions, yet ChatGPT failed to provide the necessary guidance.

The study found that:

• Weight loss recommendations - a key component in managing diabetes and metabolic disorders - were missed entirely.

• ChatGPT’s dietary recommendations lacked balance, failing to meet calorie, carbohydrate, and nutrient needs like calcium and Vitamin D.

• Advice on increasing fibre intake or using whole grains—vital for those with diabetes - was often overlooked.

This underscores that while AI can simulate a conversation, it does not have the clinical expertise required to make life-saving recommendations. Healthcare professionals should remain the final authority in providing accurate medical guidance.

The Growing Prevalence of AI in Medical Advice

Interestingly, despite their limitations, ChatGPT and other AI tools have found their way into various health-related applications. Whether integrated into fitness apps or public health campaigns, these AI systems are marketed as accessible, user-friendly ways for people to manage their health. ChatGPT, for instance, is praised for its dynamic conversational abilities, often tailored to personalised education.

However, as the study points out, there is limited research on how effective these AI-driven solutions are, especially regarding clinical nutrition and chronic disease management.

The Pitfalls of Relying Solely on AI in Healthcare

The researchers argue that AI systems should not be seen as substitutes for healthcare professionals. Relying entirely on AI poses significant risks, particularly in complex medical fields where precision and personalisation are critical.

Why the Human Element in Healthcare is Irreplaceable

AI chatbots react to prompts in a way that mimics human conversation, but they lack the critical judgment that trained dietitians and medical professionals bring. They cannot factor in nuances, long-term care plans, or complex patient histories as a human can.

What This Means for the Future of AI in Medicine

The potential of AI in healthcare is undeniable. From streamlining administrative tasks to providing real-time feedback in specific non-critical scenarios, AI holds a promising future. However, as this study reveals, the technology has a long way to go before it can be fully trusted in clinical advice.

Hi, Kevin McDonnell here. Thank you for reading my latest newsletter. Every week I share my round-up of the most interesting stories from the world of Digital Health I've been reading so you can find the ideas, people, innovations and technologies that are shaping the future of healthcare. I hope you enjoy them! Happy reading! Don't forget to follow me on LinkedIn for more daily insights.

The Government's Plan to Tackle the Hospital Activity Backlog

With the pressure on the UK’s healthcare system mounting, the government outlines strategies aimed at reducing the growing hospital backlog. The plan includes expanding capacity, improving efficiency, and exploring partnerships with private providers. However, questions about sustainability and whether these measures can alleviate systemic issues in the National Health Service linger. Addressing the backlog might just be a Band-Aid over more fundamental problems—like staff shortages or inadequate infrastructure. (75 words)

Britain’s State-Funded Health System Is Crumbling

The NHS, once a beacon of public healthcare, now faces an existential crisis. This report discusses the worsening conditions under Prime Minister Rishi Sunak’s leadership as the institution buckles under the weight of funding shortfalls, an aging population, and increasing patient demand. Long waiting times, staff burnout, and an underfunded system present challenges that threaten the future of state-funded healthcare, forcing many to wonder whether reforms can salvage this broken institution.

Healthcare Headwinds Push PE to Examine New Areas

Private equity is reshuffling its priorities in response to healthcare's evolving landscape. As traditional healthcare investment faces growing headwinds, including rising operational costs and tightening regulations, PE firms are exploring innovative areas like behavioral health, outpatient services, and telemedicine. This shift reflects a broader industry trend toward non-traditional, patient-centric solutions that promise quicker returns. Yet, the question remains: Will PE-backed healthcare ultimately drive better patient outcomes, or simply more lucrative exits for investors?

The State of the U.K. Healthcare System Under Strain

With pressures mounting, the U.K.'s healthcare system finds itself at a critical juncture. The NHS is grappling with a range of challenges: increased patient demand, financial constraints, and an overburdened workforce. While the government has pledged support, critics argue these measures are too little, too late. Can the system hold out, or is it already cracking under the weight of these demands? A deep dive into the struggles of a national institution under siege.

NHS Restricting Access to Obesity Services

A damning report reveals that the NHS is limiting access to obesity services across England, with severe consequences for patients. Despite rising obesity rates, funding cuts have caused a shortage of services, leaving many without critical support. The report highlights disparities in service provision, with vulnerable populations disproportionately affected. This comes at a time when the healthcare system is struggling to manage chronic diseases linked to obesity, such as diabetes and cardiovascular disease.

Addressing Global Health Equity Challenges

The world faces a growing health equity crisis as socioeconomic disparities widen the gap in healthcare access. This paper explores strategies for addressing these inequities, from expanding universal health coverage to tackling the social determinants of health. But with many systems already stretched thin, the challenge is how to enact meaningful change while maintaining high standards of care. It’s a multifaceted problem, requiring collaborative solutions at both local and global levels.

PM Pledges to Move to a Digital NHS

In a bold new promise, the Prime Minister commits to a digital overhaul of the NHS, following recommendations from the Lord Darzi report. The shift to digital healthcare aims to streamline processes, improve patient outcomes, and reduce administrative burdens. While the potential benefits are vast, concerns linger around data security, the digital divide, and the readiness of staff to adapt to these new technologies. Is the NHS ready for this digital transformation?

Former Verily Executives Hatch Plan to Invest Millions in High-Tech Medical Data

Former Verily executives have launched a new initiative focusing on the use of high-tech medical data to revolutionize healthcare. By investing millions in data-driven technologies, they aim to improve diagnostics, treatment outcomes, and preventive care. The initiative reflects the growing trend of leveraging data science to enhance healthcare delivery. However, questions about data privacy, integration with existing systems, and ethical concerns surrounding AI's role in healthcare remain prominent.

AI in Healthcare: The EU AI Act’s Implications

The European Union’s AI Act is set to reshape the healthcare landscape, introducing new regulations aimed at governing the use of AI in medical contexts. While AI promises breakthroughs in diagnostics and patient care, the Act raises concerns about accountability, data protection, and the balance between innovation and regulation. As healthcare providers and tech companies prepare for the impact, the industry must grapple with the complexities of ensuring ethical AI integration.

Future-Proofing Healthcare Imaging IT

With rapid technological advancements in imaging IT, healthcare organizations must prepare for an uncertain future. This article outlines key strategies for future-proofing imaging infrastructure, including adopting scalable systems, integrating AI tools, and ensuring cybersecurity. As healthcare imaging becomes more data-driven, the challenge lies in balancing innovation with reliability, especially as new imaging modalities emerge. Organizations that fail to evolve their IT strategies risk falling behind in an increasingly competitive healthcare landscape.

Join The Future Health community today! A community of HealthTech leaders finding the ideas, people, innovations and technologies shaping healthcare. Every Monday, an article exploring the future of healthcare and every Friday, a roundup of the week's news and developments. Whenever you're ready, share your news, write a guest article, sponsor the newsletter.

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Generative AI is rapidly transforming numerous industries. In healthcare, there can be no doubt that it holds immense potential for revolutionising various areas such as drug discovery, medical imaging analysis, and personalised medicine.

In a recent report from McKinsey, which surveyed 100 US healthcare leaders - 70 per cent of respondents - payers, providers, and healthcare services and technology (HST) groups - say that they are pursuing or have already implemented gen AI capabilities.

Here are a few standout examples:

• Accelerated Drug Discovery: Traditionally, drug discovery is like watching paint dry—slow and costly. Generative AI can whip up new drug molecules with desired traits, slashing the time and money spent finding candidates for clinical trials.

• Enhanced Medical Imaging Analysis: Generative AI algorithms can be taught to scrutinise medical images, such as X-rays and MRIs, with impressive precision. This helps radiologists spot issues and make better diagnoses, improving patient outcomes.

• Personalised Medicine: Generative AI can design tailored patient treatment plans by analysing their unique genetic profiles and medical histories. This means more effective, targeted therapies.

The McKinsey report sheds light on some key insights:

• Most respondents are already using or testing generative AI tools. Signalling a growing interest in this tech within healthcare.

• Measuring generative AI solutions' return on investment (ROI) is still tricky for many healthcare organisations. This highlights the need for more research to pinpoint the tangible benefits of generative AI in healthcare.

Despite its undeniable potential, generative AI's adoption in healthcare faces several hurdles. Here are some of the most prominent challenges:

• Risk and Regulatory Concerns: Healthcare is naturally cautious, and introducing AI raises safety and efficacy concerns. The regulatory landscape for generative AI in healthcare is still evolving. This will add uncertainty.

• Data Quality and Bias: Generative AI models are only as good as the data they learn from. These models can magnify biases in healthcare data, leading to inaccurate or unfair outcomes. It's crucial to tackle data quality and bias responsibly using generative AI.

• Talent Gap: Implementing generative AI effectively requires a skilled workforce that is well-versed in both healthcare and AI. The shortage of such talent will slow down the widespread adoption of generative AI in healthcare.

Despite these challenges, the benefits of generative AI in healthcare are too significant to ignore. Here's how stakeholders can support its responsible and effective adoption:

Collaboration Between Stakeholders: It is vital to encourage cooperation between healthcare providers, AI developers, and regulatory bodies. We must set clear guidelines and share best practices for generative AI in healthcare.

Emphasising Explainability and Transparency: Creating generative AI models that are easy to understand and transparent can help alleviate concerns about safety and efficacy and build the trust needed among healthcare professionals and patients.

Investing in Talent Development: Supporting educational programs and training initiatives will help close the talent gap. We need a workforce ready to tackle the complexities of generative AI in healthcare.

As the survey results show, many healthcare leaders have begun pursuing plans to adopt the technology more broadly, which strategic partnerships have enabled in part.

As gen AI deployment progresses, many surveyed leaders shared that their organisations are focused on initially using this technology to support clinically adjacent applications, with clinical and administrative efficiency and patient/member engagement surfacing as areas believed to gain the most from gen AI.

We're confident that generative AI will significantly shape the future of healthcare. The question isn't whether we should use AI but how.

Hi, Kevin McDonnell here. Thank you for reading my latest newsletter. Every week I share my round-up of the most interesting stories from the world of Digital Health I've been reading so you can find the ideas, people, innovations and technologies that are shaping the future of healthcare. I hope you enjoy them! Happy reading! Don't forget to follow me on LinkedIn for more daily insights.

Amazon's Generative AI Healthcare Push

Amazon's foray into generative AI for healthcare promises to change patient care. Yet it faces considerable obstacles. Amazon's strategy aims at integrating AI within clinical settings to enhance diagnostic accuracy and streamline operations. However, there are significant regulatory and ethical challenges that must be navigated. The potential of Amazon's AI plans could redefine healthcare, balancing technological advancement with patient-centric care.

43 Million People Affected by HealthEquity Data Breach

The recent data breach at HealthEquity has compromised the personal information of 43 million individuals. This incident underscores the escalating cybersecurity threats within the healthcare sector. Revealing vulnerabilities in data protection measures. This article covers the breach's implications, potential consequences for affected individuals, and the urgent need for robust cybersecurity protocols to safeguard sensitive health information against future breaches.

Amazon DoctorAI to Automate Routine Healthcare Tasks

Amazon's DoctorAI aims to automate routine healthcare tasks - leveraging advanced AI to support healthcare professionals. This initiative could significantly reduce administrative burdens, allowing medical staff to focus more on patient care. The article discusses how DoctorAI's capabilities extend to scheduling, patient data management, and preliminary diagnostics, positioning it as a pivotal tool in modern healthcare.

Healthcare Staffing: Challenges and Solutions

The healthcare industry faces persistent staffing challenges. This article explores various strategies being employed to address these shortages, including recruitment techniques, enhanced training programs, and the integration of technology to optimize workforce management. The focus is on ensuring sustainable staffing levels to maintain high-quality patient care and operational efficiency within healthcare facilities.

MedTech Europe Urges EC to Reform Device Regulations

MedTech Europe is calling for regulatory reforms to streamline the approval process for medical devices. This article highlights the organization's concerns about the current regulatory framework - which they argue hampers innovation and delays patient access to new technologies. The proposed changes aim to create a more efficient and predictable regulatory environment, fostering innovation while ensuring patient safety.

Widespread Support for AI Within NHS, Public Polling Suggests

Public polling indicates strong support for the integration of AI within the NHS. This article presents the findings of recent surveys, showing that the majority of respondents believe AI can enhance healthcare delivery (and improve patient outcomes). It discusses the potential benefits of AI, such as increased efficiency and accuracy, as well as the public's expectations and concerns regarding its implementation.

Majority of NHS Staff Support Use of AI, Finds The Health Foundation

A study by The Health Foundation finds that most NHS staff are in favor of using AI in healthcare. The article explores the reasons behind this support, including the potential for AI to alleviate workloads, enhance diagnostic accuracy, and improve patient care. It also addresses the challenges and reservations some staff may have about adopting AI technologies.

Generative AI in Healthcare: Adoption Trends and What's Next

Generative AI is gaining traction in healthcare, with growing adoption across various applications. This article from McKinsey examines current trends in AI implementation, from diagnostics to personalized treatment plans. It also discusses future prospects, including advancements in AI capabilities and potential barriers to widespread adoption. The focus is on how generative AI can transform healthcare delivery, improving efficiency and patient outcomes.

Which Technologies Offer the Biggest Opportunities to Save Time in the NHS

Identifying technologies that can save time within the NHS is crucial for improving efficiency. This article explores various technological innovations that hold the most promise. Including AI, automation, and digital health tools. It discusses the potential impact of these technologies on reducing administrative burdens, streamlining workflows, and enhancing patient care, emphasizing the importance of strategic investment in tech to optimize NHS operations.

AI in the NHS: Shaping the Future of Healthcare

The Health Foundation's event on AI in the NHS focuses on how artificial intelligence is reshaping healthcare delivery. This article provides an overview of the discussions and key insights from the event, highlighting the potential of AI to revolutionize patient care, improve efficiency, and support healthcare professionals. It also addresses the challenges and ethical considerations involved in implementing AI within the NHS.

Join The Future Health community today! A community of HealthTech leaders finding the ideas, people, innovations and technologies shaping healthcare. Every Monday, an article exploring the future of healthcare and every Friday, a roundup of the week's news and developments. Whenever you're ready, share your news, write a guest article, sponsor the newsletter.

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Hi, Kevin McDonnell here. Thank you for reading my latest newsletter. Every week I share my round-up of the most interesting stories from the world of Digital Health I've been reading so you can find the ideas, people, innovations and technologies that are shaping the future of healthcare. I hope you enjoy them! Happy reading! Don't forget to follow me on LinkedIn for more daily insights.

• Labour’s Plans for Cybersecurity, Digital, and Data – Prime Minister Keir Starmer’s plans to introduce legislation for cyber security, digital and data were outlined in the King’s Speech on 17 July 2024. King Charles also announced a Digital Information and Smart Data Bill, which will enable new, “innovative” uses of data to help boost the economy.

• Google’s Healthcare Strategy and the Role of AI - Google is developing digital platforms, forming partnerships with healthcare providers, and promoting disease prevention. Generative AI models will be a key part of their efforts, said Karen DeSalvo, the tech giant's chief health officer.

• Innovations in Bioengineering for Regenerative Medicine - A new study by researchers at NYU Grossman School of Medicine shows that an AI tool can draft responses to patients' EHR queries as accurately as their human healthcare professionals, and with greater perceived 'empathy.' suggesting that chatbots could reduce the workload of care providers.

• New UK Government Bills for AI, Cybersecurity, and Resilience - The King stated his government will “seek to establish the appropriate legislation to place Requirements on those working to develop the most powerful artificial intelligence models.”

• NHS Signs £20m Cloud Deal for System Continuity - NHS England has spent about £20m on a short-term contract for cloud hosting provision to ensure continuity for core services while a procurement process for a new agreement is conducted. The central health-service organisation has retained IT reseller Softcat for an eight-month deal for “cloud consolidation services”. The engagement, which was awarded on 28 May, came into effect on 1 June and runs until 31 January 2025, according to a newly published contract award notice.

• Transforming NHS Mental Health Care through Voluntary Sector Partnerships – A new evaluatin, led by the Centre for Mental Health, looked at a series of ‘alliances’ established by the charity Rethink Mental Illness using funding from the Charities Aid Foundation. The evaluation — More than a sum of our parts — examined the impact of these alliances in Coventry and Warwickshire, North East Lincolnshire, Sheffield and Tower Hamlets. It found that these services allowed their output to be tailored by those with lived experience of mental health issues, thus making services more effective and accessible.

• ADHD Digital Test Approved for NHS Use in England and Wales – The National Institute for Health and Care Excellence (Nice) has issued draft guidance approving the use of the QbTest by psychiatrists and specialist children’s doctors. The computer-based test measures the three main characteristics of ADHD: inattention, impulsivity and hyperactivity.

• UK’s First AI Physio Clinic Trialled by NHS Lothian - Flok Health provides same-day access to automated, responsive video appointments with an AI physiotherapist via a smartphone app. Flok is the first platform of its kind to have been approved by the Care Quality Commission as a registered healthcare provider, creating a brand new treatment pathway for physiotherapy patients.

• RLDatix Acquires CareBeans - By combining RLDatix’s and Carebeans’ strengths, the company offers a comprehensive platform that addresses the critical needs of the industry. The combined offering provides healthcare organizations with a single platform for care planning and management, 24/7 compliance, workforce optimisation and specialised care support.

Join The Future Health community today! A community of HealthTech leaders finding the ideas, people, innovations and technologies shaping healthcare. Every Monday, an article exploring the future of healthcare and every Friday, a roundup of the week's news and developments. Whenever you're ready, share your news, write a guest article, sponsor the newsletter.

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Hi, Kevin McDonnell here. I share an article exploring the future of healthcare every week. Don't forget to follow me or my page for more insights every day.

The medical device industry is experiencing a significant surge in mergers and acquisitions (M&A) activity in 2024. Global data reveals a buoyant market driven by several key themes. In this article, we'll explore the factors propelling this trend and offer insights into how companies can develop a strategic approach to M&A in this market.

A Market Ripe for Consolidation

One of the primary drivers of M&A activity is market consolidation. The medical device market is fragmented, with numerous players competing across various segments. Companies are looking to expand their product portfolios and market share through strategic acquisitions. This allows them to offer more solutions to healthcare providers and patients, ultimately strengthening their competitive position.

For instance, Ingersoll Rand's recent acquisition of Ilc Dover, valued at $2.3 billion, exemplifies this trend. This deal expands Ingersoll Rand's portfolio in the attractive anaesthesia delivery market.

Innovation Through Acquisition

Technological advancements are another significant factor driving M&A. Larger companies increasingly acquire smaller, innovative firms to access cutting-edge technologies and products. This allows them to stay ahead of the curve and respond to the evolving needs of the healthcare sector.

Take, for example, the field of pulsed-field ablation (PFA) - a minimally invasive technology for treating cardiac arrhythmias. Major players like Medtronic and Boston Scientific have recently secured FDA approvals for their PFA systems, highlighting the growing interest in this area and the potential for further M&A activity to accelerate market penetration.

Navigating the Regulatory Maze

The regulatory landscape surrounding medical devices is constantly evolving. Companies are facing increasingly complex regulations and approval processes. This is prompting some companies to acquire others with established compliance frameworks, ensuring a smoother path to market for new technologies.

M&A as a Growth Engine

Global expansion is another crucial theme driving M&A in the medical device market. Companies use acquisitions as a strategic lever to enter new geographic markets and expand their international presence. This allows them to tap into faster-growing emerging markets and capitalize on the increasing demand for medical devices globally.

For instance, we're witnessing a rise in foreign direct investments (FDI) in the medical device sector. Data shows that FDI-related deals accounted for 39% of global M&A activity in Q1 2024, highlighting the growing interest from international investors.

Thematic Focus

Beyond the headline figures, a closer look reveals a trend towards M&A activity focused on strengthening positions in key thematic areas that are shaping the future of the medical device industry. This includes digital health, connected devices, and minimally invasive surgical technologies.

Companies are strategically acquiring capabilities in these areas to ensure they remain competitive in the long run. While not explicitly mentioned in the data, the rise of digital health and connected devices is undoubtedly a significant underlying factor driving M&A activity.

A Catalyst for Deals

The overall market performance of the medical device industry is also contributing to the M&A surge. The global medical devices market witnessed deals worth a staggering $47 billion in Q1 2024, representing a substantial increase compared to the previous year. This robust market performance is creating a favourable environment for M&A activity.

The Rise of Mega-Deals

Another noteworthy development is the trend towards larger, more impactful acquisitions. The sector recorded a significant increase in mega-deals (valued at over $1 billion) in Q1 2024 compared to the previous year. This suggests that companies are increasingly willing to make bold strategic moves to solidify their positions in the market.

New Frontier

The medical device industry is witnessing a surge in M&A activity targeting emerging markets. Countries like China, India, Brazil, and Russia offer faster growth opportunities due to their large patient populations and rising healthcare spending. These markets are particularly attractive for the mid-tier segment, where local companies have developed "good enough" quality products at competitive price points.

The Rise of Nearshoring

Concerns about supply chain disruptions are prompting companies to focus on acquiring facilities closer to their primary markets. This trend, known as nearshoring, allows for greater control over production and mitigates potential risks associated with global supply chains.

A Strategic Approach to M&A in a Dynamic Market

The current M&A landscape in the medical device industry presents both opportunities and challenges. Here are some key considerations for companies looking to leverage M&A as a strategic growth driver:

• Clearly defined strategic goals: Companies must clearly understand their long-term strategic goals before embarking on an M&A journey. What are the specific areas they want to expand into? Are they looking to acquire new technologies, enter new markets, or gain access to complementary product lines? Defining these goals upfront will guide target selection and ensure the acquisition aligns with the overall business strategy.

• Target selection with a critical eye: Companies must evaluate potential targets once strategic goals are established. A thorough due diligence process is essential, assessing the target's financial health, technological capabilities, market position, and cultural fit. Companies should avoid the temptation to be swayed by headline-grabbing deals and focus on targets that demonstrably contribute to their long-term objectives.

• Integration planning is critical: A successful M&A goes beyond simply signing the deal. Companies must invest significant time and resources into planning the integration process. This includes cultural integration, aligning product development roadmaps, and optimizing operational efficiencies. Effective communication and change management are crucial to ensure a smooth transition and maximize the value derived from the acquisition.

• Digital transformation considerations: The medical device industry is undergoing a digital revolution. Companies evaluating potential targets should consider their digital capabilities and how they align with their own digital transformation strategy. This includes factors like the target's use of data analytics, investment in connected devices, and overall digital maturity.

• Navigating the regulatory landscape: Regulatory considerations are paramount in the medical device industry. Companies must ensure the target's products and processes comply with all relevant regulations. Integrating the target's regulatory compliance framework into the acquiring company's system is essential to avoid delays and disruptions in product development and commercialization.

• Financial considerations and deal structuring: M&A transactions can be complex financially. Companies should carefully evaluate the target's valuation, financing options, and potential tax implications. Structuring the deal effectively can significantly impact the overall return on investment.

• Building a strong M&A team: A successful M&A strategy requires a dedicated and experienced team with expertise in deal structuring, due diligence, integration planning, and regulatory affairs. Companies may need to augment their internal team with external advisors to ensure they have the necessary skill sets to navigate the complexities of M&A transactions in the medical device industry.

The Future of M&A in Medical Devices

The M&A landscape in the medical device industry is likely to remain dynamic for the foreseeable future. Several trends are expected to shape this landscape in the coming years:

• Continued focus on innovation: The rapid pace of technological advancements will continue to drive M&A activity as companies seek to acquire cutting-edge technologies and remain at the forefront of innovation.

• Emerging markets take centre stage: The growth potential of emerging markets will continue to attract investment, with M&A playing a key role in facilitating market entry and expansion for medical device companies.

• Convergence with digital health: The convergence of medical devices with digital health technologies will likely lead to a new wave of M&A activity as companies seek to develop comprehensive solutions that integrate hardware, software, and data analytics capabilities.

• Focus on value-based care: The growing emphasis on value-based healthcare delivery models is expected to influence M&A activity. Companies are likely to target assets that enable them to deliver cost-effective solutions and demonstrate the clinical value of their products.

M&A can be a powerful tool for growth and innovation in the medical device industry. However, it's crucial to approach M&A strategically, with a clear understanding of the drivers, challenges, and best practices involved. By carefully considering the abovementioned factors, companies can leverage M&A to strengthen their market position, expand their capabilities, and deliver greater value to patients and healthcare providers in this ever-evolving market.

A significant change is taking place in the healthcare sector. Artificial intelligence (AI) is fast becoming a crucial component of providing healthcare, making choices, and enhancing patient outcomes. However, this technology also brings several difficulties and moral dilemmas that must be resolved if the benefits it can bring are not to get in the way of adoption.

Healthcare AI goes beyond robots doing surgery (though that is also a growing area). It involves processing enormous volumes of medical data, identifying patterns people might overlook, and creating predictions that could save lives.

• The global AI in healthcare market was valued at $19.27 billion in 2023 and is expected to grow at a CAGR of 38.5% from 2024 to 2030.

• According to a study published in Nature Medicine, an artificial intelligence system outperformed human radiologists in detecting breast cancer in mammograms, lowering false positives by 5.7% and false negatives by 9.4%.

• A study published in JMIR Formative Research developed an algorithm called NAVOY Acute Kidney Injury that can predict the onset of AKI in ICU patients 12 hours before it occurs, with high accuracy (AUROC of 0.91)

• DeepMind, an Alphabet company, developed an AI algorithm that could predict acute kidney injury up to 48 hours before it occurred with 90% accuracy. This study involved analyzing medical records from over 700,000 patients in the Department of Veterans Affairs healthcare system

This shows how quickly Healthcare AI is impacting patient care and results.

AI is being used in healthcare in all areas, from diagnosis to treatment. Here are seven distinct and obscure ways artificial intelligence is changing healthcare:

AI that examines each individual's genetic composition, lifestyle choices, and medical background can suggest treatment regimens that are specifically suited for each patient.

AI can process and analyse enormous volumes of biological and chemical data to identify and forecast the efficacy of possible new medications, potentially saving years of research and development.

AI can more than just find anomalies; it can also measure how medical imaging changes over time, which can be used to monitor the course of a disease or the efficacy of a treatment.

AI models forecasting patient admission rates can help hospitals optimise personnel and resource allocation through predictive analytics.

AI-driven chatbots can offer round-the-clock patient assistance, respond to inquiries, remind patients to take their prescriptions and notify real HCPs when needed.

AI can reduce administrative burden and improve accuracy by automatically adding pertinent data from doctor-patient talks to EHRs.

However, using AI in healthcare remains challenging. There are a lot of obstacles and moral issues to consider:

Healthcare data is very sensitive. How can we guarantee that AI systems preserve patient privacy while utilising massive data?

AI systems may reinforce or worsen healthcare disparities if educated on biased data sets. AI must not discriminate based on socioeconomic class, gender, or race.

The opaque decision-making process of many AI algorithms, especially deep learning models, is known as the "black box" problem. How can we ensure AI systems are responsible and answerable in the healthcare industry, where choices might mean the difference between life and death?

Medical professionals may over-rely on AI, which could impair their clinical judgement or expertise.

What rules apply to AI in healthcare? Current frameworks are not built for adaptive algorithms that are constantly learning and evolving.

I think we need robust regulation and frameworks if we are going to see AI adoption.

Transparency - AI systems should be as open and honest as feasible, with detailed documentation of their algorithms, training sets, and decision-making methods.

Bias testing - To identify and address any biases in AI systems, routine audits should be carried out.

Human oversight - AI in healthcare should support human decision-making, not take its place. Human scrutiny should always be involved in essential choices.

Constant observation - AI systems must have their performance and unexpected effects constantly observed.

Consent from the patient - When AI is being used in a patient's care, the patient should be informed and given the choice to refuse it.

Multidisciplinary cooperation - In addition to technologists, HCPs, ethicists, and patient advocates should be involved in developing healthcare AI.

Regulatory adaptation - We must collaborate with regulatory organisations to create frameworks that can keep up with rapidly advancing AI technology.

We're just starting to learn how AI might change the healthcare industry. It might enable us to shift healthcare from reactive to proactive by foreseeing and preventing illnesses before they arise. AI-powered telemedicine might democratise access to high-quality healthcare by delivering expert-level diagnoses to underprivileged areas. Additionally, handling regular administrative duties can allow healthcare providers to concentrate more on patient care.

But technology advancement alone won't be enough to realise this potential. It calls for a careful, moral strategy that prioritises the needs of the patients. Collaboration between technologists, medical professionals, legislators, and patients is necessary. It also necessitates a readiness to address challenging issues like privacy, autonomy, and the use of technology in healthcare.

In my opinion, AI alone won't determine the future of healthcare.

Our use of AI will.

In my opinion, there's a lot of misplaced hope that technology will be a silver bullet for the NHS's woes.

Don't get me wrong. Technology can be (and is) a hugely powerful tool.

But are we looking in the wrong place?

We all saw the headlines about the recent £3.4bn tech funding for the NHS to boost productivity. Sounds great, right?

However, the Health Foundation has been diving deep into this question, and what they are finding is much more nuanced than the announcements suggest.

Clinicians Want Better Basics, Not Sci-Fi Solutions

Forget fancy AI or robots operating on patients (at least for now). When the Health Foundation surveyed over 500 clinical staff, they said the technologies that saved them the most time were the most unglamorous - EHRs and communication tools.

But even with EHRs, there's a catch. Many trusts miss out on the full benefits because of poor implementation and user experience. This highlights a key point. It's about more than just having the tech. It's about using it well.

The Health Foundation survey revealed a lack of IT support, funding, and even basic connectivity as significant hurdles.The government needs to fund the "change," not just the "tech."

Time Saved Isn't Time Gained for Patients (Automatically)

Even if technology frees time, it does not guarantee more patient care. The Health Foundation survey showed that only 27% of freed-up time would likely go towards direct clinical activity.

This doesn't mean it's all wasted! Clinicians might use the time for longer consultations, improving the quality of care, or for crucial professional activities like training and quality improvement.

The "Time for Care" Myth Needs to Die

Policymakers often assume freed-up time automatically equals more patient throughput. But that's a dangerous oversimplification. We need a broader view of how technology can improve productivity.

Leaders and employers should offer staff a compelling deal: use some freed-up time for activities that benefit productivity and improve job satisfaction, like training or research.

Perhaps we should ditch the "productivity fairy" and focus on practical solutions that empower staff and improve care, not just throughput?

What would you add?

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