Regulators from across the globe have agreed on a shared vision for the future of artificial intelligence in healthcare, following new commitments revealed during the AIRIS 2025 Symposium in Incheon.
The discussions, which were detailed in Lab Manager’s coverage of the event, centered on how governments and industries can build AI systems that are not only powerful but also ethical, equitable, and safe across their entire lifecycle.
One of the key outcomes was a growing consensus that AI regulation in health must move beyond pre-market approvals and evolve into a continuous process of oversight, updating, and transparency.
The meeting emphasized that an algorithm’s responsibility doesn’t end once it’s deployed — it must be monitored as it learns and adapts.
The World Health Organization’s call for a collaborative approach echoed that sentiment, urging countries to align frameworks that prioritize patient safety and equity over speed.
That’s the part that makes me pause. AI has already revolutionized diagnostics, from early cancer detection to predictive triage, but the idea of “regulating learning systems” feels like chasing a moving target.
And yet, it’s the only way forward if we want trust. A recent perspective from EY on the escalating challenge of regulating health AI warns that without continuous validation and accountability, even well-intentioned algorithms can drift into bias or error over time.
There’s another layer here: access and fairness. The WHO’s own Global Initiative on AI for Health is working to ensure developing countries aren’t left behind as AI transforms care delivery.
That’s not just a moral imperative; it’s practical. If only wealthy health systems can afford the regulatory compliance required to use AI safely, global inequality will deepen.
And that, to me, is the quiet warning hiding between the lines of these announcements.
Still, it’s not all red tape and bureaucracy. Smart regulation could actually boost innovation.
When companies know the standards from the start — when they can design for compliance instead of scrambling for it — they move faster.
That’s the thinking behind new frameworks discussed at the symposium, which focus on risk-based classification and lifecycle management rather than one-size-fits-all bans or approvals.
Similar to what’s being tested in Europe under the EU AI Act, this approach might be the key to balancing safety with speed.
As someone who’s been watching this space closely, I can’t help feeling a bit optimistic.
The momentum toward coordinated oversight — from Lab Manager’s account of regulators setting next steps to WHO’s global health initiatives — signals that AI in healthcare is finally maturing.
Sure, it’ll be messy, slow, and full of debate, but maybe that’s exactly what progress looks like when lives are on the line.
