Trust, Talent & the Trajectory of AI in Healthcare
This week, I found myself pulled into a set of articles that centre around a recurring tension in healthcare innovation: the gap between technological capability and human acceptance. It’s clear that AI is no longer an experiment in healthcare - it’s rapidly becoming part of its infrastructure. But the ways in which it’s being integrated - and how people respond to it - are still very much being figured out.
One of the more compelling perspectives came from an Open Access Government article, which outlined how patient acceptance of AI increases significantly when clinical experts remain closely involved. That may sound obvious, but it underscores an important nuance: technology can’t lead the conversation without trust, and trust in healthcare still lives squarely with humans. The article goes on to show that when doctors visibly validate the AI’s role in diagnosis or treatment, patients are far more likely to accept its input.
That ties in well with a Fortune article that expands on this theme of trust. It emphasizes that no matter how advanced the technology gets, adoption won’t happen without confidence in its safety, ethics, and transparency. And that trust isn’t just built on outcomes - it’s built through processes, communication, and cultural change within organizations.
Those themes showed up in different ways across the rest of what I read. For instance, OpenAI’s new HealthBench initiative is trying to create a more rigorous and clinically relevant testing environment for health-focused AI models. It’s another reminder that standardization and accountability need to move at the same speed as innovation - otherwise we’ll keep running into adoption walls.
Meanwhile, we’re seeing the rise of powerful new AI models outside of Silicon Valley as well. Alibaba’s healthcare AIhas now scored at senior doctor levels on medical licensing exams. That’s both impressive and slightly uncomfortable - because even if the model is accurate, its path to clinical use will still hinge on whether it can gain the confidence of providers and regulators.
That broader readiness was on full display at Imperial College’s AI-powered healthcare showcase. The demos ranged from diagnostic tools to voice-driven systems that reduce administrative burdens for clinicians. What stood out to me was how much emphasis the presenters placed on speed and usability - these aren’t research projects anymore, they’re being actively positioned as production-ready systems. And yet, everyone on stage kept returning to the same theme: “We need the clinicians to want this.”
On the investment side, the UK’s HealthTech momentum continues. Meridian Health Ventures just launched a €44 million transatlantic fund to back HealthTech startups scaling between Europe and the US. That’s a big signal of confidence in the sector’s future. At the same time, we saw some sobering context in Digit’s breakdown of 2024 UK HealthTech funding - which, while strong in absolute terms, was more concentrated among fewer deals, especially those tied to AI or infrastructure.
That trend was echoed in HealthTech Zone’s writeup on the real drivers of HealthTech growth. The takeaway? It’s not just hype or investment cycles - it’s demand from providers who are being squeezed by burnout, cost pressures and administrative overload. The tools that win are the ones solving real operational pain points - not just promising transformation.
Speaking of operational change, the Centers for Medicare & Medicaid Services are now signalling some big shifts. A recent RFI issued in partnership with ASTP suggests a much more proactive stance on how regulatory frameworks might evolve to support AI and digital health tools. This is significant - not just for compliance, but because regulatory clarity is often the tipping point between proof of concept and actual deployment.
And then, to wrap the week, Epic announced Launchpad - a platform aimed at helping healthcare providers adopt generative AI tools more quickly. Epic’s presence across hospitals in the US gives them massive distribution power, so if they can help lower the barrier to entry for AI, we may finally start seeing some real adoption at scale.
Putting it all together, it’s clear we’re well past the “potential” phase of healthcare AI. The challenge now is trust: building it with patients, regulators, and providers. That takes more than just accuracy and performance - it requires clarity, transparency, and partnership. We’ll likely see a new divide form in the market - not just between good and bad AI, but between AI that’s clinically accepted and AI that isn’t.
Would love to hear your take - especially if you’re seeing examples of tools that are crossing that trust chasm well. Have a great weekend.
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