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There’s been a lot of chatter lately about the NHS’s new announcement: ambient voice technologies that generate clinical summaries will now be classified as medical devices. In plain terms, that means these tools—often powered by generative AI—will be regulated just like other medical devices, with stricter oversight and safety requirements.

In this piece, I’ll break down what the NHS actually said, share a few principles I believe should guide AI governance, and explore what similar rules could mean for physicians, patients, and the U.S. healthcare system.

Shout out Rik Renard for publicizing this on LinkedIn!

On April 27, 2025, NHS England first announced it: ambient scribing tools that use generative AI—like those that listen to clinician-patient conversations and generate a medical note—will be classified as medical devices.

This is a big deal. It means that once these tools go beyond simple transcription and start summarizing, coding, or suggesting next steps, they’re regulated technologies with real clinical impact. They’re no longer just documentation aids.

The updated guidance appears to be a response to growing adoption—and misuse. According to NHS officials, several vendors were already deploying these tools in clinical settings without proper registration or regulatory oversight, raising safety and compliance concerns.

The guidance outlines a long list of requirements for these tools:

If a product’s generative AI component starts suggesting diagnoses, recommending treatments, or creating outputs that guide medical decision-making, it must go through regulatory approval—just like any other medical device.

So, while AI may help write the note, clinicians (you and me!) still own what’s in it—and the system needs a framework to ensure that what’s written is safe, accurate, and appropriate.

Regulating generative AI is a different beast than a medical device.

You’re not approving a pacemaker or a blood pressure cuff. You’re trying to set guardrails around a constantly evolving, black-box system that can respond to virtually any clinical question—sometimes accurately, sometimes with made-up nonsense.

I’m drawn to a proposal outlined in NEJM AI: maybe the best way to govern generative AI isn’t to treat it like a device, but to treat it more like a clinician.

Think about it. We don’t expect doctors to be perfect. But we do require licensing (I just dropped $1k on my license), training (I’m about to start year 7 of training), supervision (I’m still not running solo), continuing education, and re-evaluation. We hold clinicians accountable through malpractice liability and professional standards. Why should we expect less from a model that claims to assist—or even augment—clinical decision-making?

So here are a few principles I think should guide AI governance:

What I’ll continue to ask myself is how can we ensure the AI we use in healthcare is safe, effective, and actually improves care without eroding trust or accountability.

AI is here to protect patients, empower physicians, and build a health system that works exponentially better than does now. That’s how I look at it.

For patients, they’re the ones most vulnerable when things go wrong—and the least involved in how these technologies are implemented. If an AI scribe misrepresents what a patient said, drops a key detail, or infers something that wasn’t intended, the patient may never know. But it could affect how they’re treated, what medications they get, or whether a diagnosis is missed. Patients have a right to high-quality care, and that includes confidence that any technology used in the process has been held to a high standard. Just because a note was generated by an AI doesn’t mean it should be treated with any less scrutiny.

For physicians, the stakes are different. The promise of AI is better workflows and less time charting. But that only holds true if the technology works well and integrates seamlessly. If you spend more time fixing a note than you would have writing it yourself, is that really progress? Worse, if you’re held responsible for the content but didn’t even create it, that’s a recipe for burnout and legal risk. Physicians need tools that lighten the load—not shift liability or introduce new headaches. They also deserve transparency: how does the model work? Can you trust it to get the terminology right? Can it handle nuances in your specialty or your patient population?

And then there’s the health system. AI implementation needs to make care safer and more cost-effective. That means integrating with the EHR, working across departments, and actually improving documentation quality—not just spitting out pretty prose (looking at you ChatGPT). It also means asking hard questions: Does this tool reduce errors? Does it help clinicians be more efficient? Does it avoid bias and protect patient privacy? If not, what are we doing?

Ultimately, this is the tension at the heart of generative AI in healthcare: we want it to be fast-moving and innovative, but we also need it to be safe and accountable.

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