Healthcare is built on a dynamic and deeply human foundation. It’s called the practice of medicine for a reason: it evolves; it adapts; it learns in real time.
Competent oversight of AI in healthcare matters not just technically, but also ethically. When you’re dealing with human lives, when models are being used to triage, diagnose, or allocate care, mistakes aren’t just errors. They have real consequences for real people.
These high stakes demand a governance framework that keeps evolving alongside technologies and the people they serve.
Understanding AI in healthcare
AI is becoming a defining technology in healthcare. In this context, it is not a single tool, but rather a spectrum of capabilities, some ambient, some predictive, some procedural. Many are embedded within workflows that are themselves constantly adapting.
Importantly, many AI solutions are already being utilized in various clinical applications, from autonomous coding and predicting patient risks to ambient listening in exam rooms. AI agents make triage decisions, document visits, predict risks, and much more.
In short, these systems now have a direct impact on patients, determining who gets care when and how that care is evaluated. They even determine how healthcare professionals get paid.
Meanwhile, today’s regulatory frameworks for AI in healthcare have become inadequate to address these use cases and the even more advanced ones that AI promises for the future.
The problem with current oversight
Many of the current regulatory models for AI in healthcare are retrofitted from clinical trials or device approval structures. They assume stasis.
AI doesn’t stand still, however. It learns. It retrains. It interacts with human variability in ways that can’t always be predicted. Trying to regulate AI in healthcare as if it were a fixed, linear product like a pill or a pacemaker completely misses this reality.
Another problem is that today’s regulatory models involve metrics of “quality” that are built around averages. For this reason, most AI models dedicated to care delivery or used for compliance regulations are, unfortunately, likewise based on population-level abstractions and nested within constructs of uniformity.
Yet there is no average patient. No being is the same, and that is true genetically, environmentally, and behaviorally. Delivering an excellent patient experience and precision medicine requires a personalized approach — not classifying someone on a taxonomy.
The harmful consequences of the current approach
Building static frameworks around technologies that are still in motion, treating algorithms as absolutes, and forming policies around fictional averages can distort assessments of healthcare’s effectiveness. If we use models based on the past to assess quality measures or to set reimbursement penalties, then bias and error will run rampant in all their glory.
For example, judging and penalizing health systems and clinicians for treating patients who fall outside statistical norms dehumanizes patients. When algorithms are applied with little consideration for individual complexity, clinicians are disincentivized to embrace and manage clinical complexity. Without adaptive oversight, the consequences will cascade.
Examples of patients at particular risk of being misclassified, deprioritized, or misunderstood include people living in rural, underserved communities and those from marginalized populations, especially when health equity models are not trained on these communities. This misclassification would likely translate into suboptimal outcomes for these patients and an increasing loss of trust in health institutions.
This outdated regulatory approach also threatens to throttle AI’s ability to help healthcare providers deliver personalized medicine on a level never before seen in history. To unleash AI’s potential, a different approach is necessary.
A new approach to AI oversight in healthcare
To be effective, frameworks for AI oversight in healthcare must evolve, adapt, and learn in real time, just like these technologies themselves. Oversight is important, but it can’t be rigid. It has to be iterative and dynamically governed with humility, an eye to context, and a deep respect for the complexity of personalized care.
Some proposals for new approaches to AI oversight in healthcare have already been floated, such as AI nutrition labels, audit trails, and algorithm registries. While these ideas are well-intentioned, they too often focus on point-in-time validation, which only provides a snapshot of what’s happening. As a result, they are insufficient to understand the dynamics that underlie correlations or to identify causal factors.
What’s needed instead is an ongoing evaluative posture, one that invites continuous feedback from clinicians, patients, and implementers on the ground. In other words, authorities should engage in a constant, flexible, and responsive process of regulatory learning. Real-time surveillance, not retrospective audits, should be central to governance. This kind of oversight would serve as a guide to ensure that patient care remains at the center of the mission to integrate AI in healthcare.
The benefits of getting this right could be extraordinary. Healthcare could become more effective in numerous ways, including AI technology that enhances clinicians’ clinical judgment, automates clinical documentation, and closes the care gaps that inequities widen. For instance, studies have shown that AI can identify patterns that are subtle or invisible to the human eye, improving the accuracy of results in mammograms, colonoscopies, and more.
The time for regulatory learning is now
The most powerful force in care is the relationship between the patient and the provider. Our oversight frameworks should protect and enhance that, not obscure or override it. Responsible AI in healthcare should not be about control. It should elevate awareness of context, individual variability, evolving evidence, and the uniquely human experience at the center of every health system.
Governance must be grounded in the actual practice of medicine, not the idealization of it in classification systems and averages. We need to respect patient individuality, the evolving nature of clinical care, and the necessity of learning through practice. Toward that end, government regulators should learn and grow along with the technology and govern it dynamically, adjusting as AI evolves.
The moment is urgent, as healthcare organizations of all kinds are implementing AI across their operations. The time to adopt regulatory learning in AI oversight for healthcare is now.
