From Bias to Breakthrough: A Snapshot of Where AI in Healthcare Is Heading
This week I spent a lot of time thinking about the promises and pressures AI is putting on healthcare. Particularly the tension between what’s technically possible and what’s responsible. A good place to start is with a new study covered by Reuters that lays bare the uncomfortable truth that AI, like the humans who build and train it, can carry medical biases. It’s a timely reminder that while AI tools can spot patterns and optimize care delivery, they also risk reinforcing existing disparities if we’re not careful with data and design.
That concern is echoed in a recent Open Access Government article, which digs into public attitudes around AI in healthcare. Trust is clearly a major factor. Most people support the use of AI in limited, supervised roles but remain wary of full autonomy. This gap between technical capacity and public confidence might be one of the most defining constraints on AI’s growth in this space.
Meanwhile, the University of York released a white paper that adds an interesting angle: the “burden” AI might place on patients. Essentially, there’s a concern that the proliferation of AI could shift responsibility to individuals. Whether that’s through algorithm-led self-monitoring, digital triage, or managing one’s own data. It raises the broader question of whether we're using AI to support patients, or subtly offloading more onto them.
That said, historical parallels can offer perspective. An article from IOplus compared the emergence of AI to the introduction of modern sewer systems. Back then, infrastructure fundamentally reshaped public health. The argument here is that AI could play a similar role if applied with the same systemic intent, not just as a tech bolt-on. That struck a chord with me. Public health breakthroughs don’t happen by accident; they require real investment, structure and oversight.
Speaking of investment, there’s no shortage of capital flowing into the AI-healthcare space. InsightAce Analytics projects aggressive growth in “agentic AI,” the next frontier where systems act with increasing independence. It's ambitious, but the same questions remain: How do we ensure safety and fairness before speed?
There are at least some positive signs of alignment between tech potential and responsible execution. Google’s AI for Health Accelerator just awarded one of only three UK spots to a Newcastle-based AI healthtech firm. It’s encouraging to see smaller players from regions outside the usual tech hubs being recognized and supported.
London, on the other hand, is going big. Literally. Echelon Health’s body-scanning startup Neko just launched a massive new clinic in Mayfair. It blends full-body MRI and AI diagnostics in a wellness-centered package, aimed squarely at a premium market. It’s definitely pushing the edge of preventative care, but it also prompts the question of access. How many people will this actually reach?
At the policy level, AI is also getting a vote of confidence. The proposed Health Tech Investment Act would expand Medicare coverage to include AI-powered medical devices. If passed, this would be a major lever to bring AI into everyday healthcare, not just niche use cases. It’s one thing for technology to exist; it’s another for it to be reimbursable and accessible.
Interestingly, this momentum isn’t just in major markets. Ingenix, a Polish healthtech startup, just closed a €9M funding round to scale its AI-driven diagnostic tools. A reminder that innovation isn’t only top-down. It’s being driven by smaller players with sharp focus and speed.
Lastly, there’s an encouraging ecosystem element taking shape. Hale House, a new innovation hub on London’s Harley Street, just opened its doors to early-stage healthtech companies. It’s always been an address associated with elite medicine, but now it's also a space for cross-pollination - startups, researchers, investors all under one roof.
If there’s a thread that runs through all of this, it’s the need for balance. Between optimism and caution, automation and human oversight, innovation and inclusion. AI might not cure healthcare’s structural challenges on its own, but if we build it thoughtfully, it could ease some of the burden. That’ll depend less on what’s technically feasible and more on the decisions we make in how it's deployed, who has access, and what values guide its use.
Would love to hear what stood out to you. Where do you think the greatest risks (or opportunities) lie as AI becomes more entrenched in how we think about health?
Until next week,
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1moIt may be an asset in some markets. However In healthcare I see risk. I had my first experience with AI yesterday and I was not impressed. A lot more to be done as it relates to healthcare.