Reinforcement learning in artificial intelligence for health. Opportunities, risks, and implications for policy and practice (Accelerating the digital transformation of the health sector in the Americas)

Reinforcement learning may sound complex, but the core idea is simple. Imagine a system that tries different actions, observes what happens, and over time gravitates toward the actions that tend to produce better results. It does not follow a fixed set of rules; instead, it adjusts its behavior based on feedback. Each time an action leads to a favorable outcome, that action becomes more likely to be repeated. Each time it does not, it becomes less likely. Through many repetitions of this cycle, the system develops a pattern of behavior oriented toward the outcomes it has been designed to pursue. 

In health systems, this distinction becomes particularly important because decisions are rarely isolated. A treatment decision affects a patient’s condition, which influences the next decision, and so on. Reinforcement learning is designed to learn from these sequences, identifying strategies that lead to better outcomes over time. At the same time, this creates the possibility that any pattern present in the data can be reinforced repeatedly. If certain groups have historically received different levels of care, or if certain actions have been more common regardless of their appropriateness, reinforcement learning systems may reproduce those patterns, not because they are correct, but because they are statistically overrepresented in the training data. 

Understanding these concepts is therefore essential for engaging with reinforcement learning in a way that is informed, responsible, and capable of strengthening health systems, ensuring that these technologies contribute not only to better decisions but to more reliable, adaptive, and effective systems that serve all populations. This understanding must be grounded in an adaptive agile approach to governance – one that combines regulatory flexibility with iterative responsiveness, enabling institutions to adjust frameworks based on real-time evidence in volatile technological environments, rather than rigidly adhering to fixed rules designed for static systems. This principle must be developed simultaneously and in a way that is compliant with the protection of fundamental rights, recognizing that technical progress and the safeguarding of human rights are not competing objectives but mutually reinforcing conditions for responsible innovation in health.