Extending the Fundamental Theorem of Biomedical Informatics for the AI era

Charles Friedman's Fundamental Theorem of Biomedical Informatics holds that a person working in partnership with an information resource outperforms that same person unassisted. Since its publication, advances in artificial intelligence (AI), adaptive learning systems, and large-scale data infrastructures have transformed the biomedical ecosystem, extending informatics beyond clinical care into domains such as public health, consumer health, translational science, and the broader life sciences. Such expansion has further underscored the importance of the Fundamental Theorem while also elucidating ways it can be expanded to meet current needs.

To reassess and extend the Fundamental Theorem for the AI era in a manner that preserves its conceptual strength while broadening its applicability across an evolved and more complex biomedical ecosystem.

This Viewpoint synthesizes empirical evidence and sociotechnical theory related to human-AI collaboration, learning health systems (LHS), learning public health systems (LPHS), AI governance, and systems science to contextualize the Fundamental Theorem within such contemporary frameworks.

We argue that the unit of analysis of the Fundamental Theorem should shift from individuals and tools to adaptive sociotechnical systems spanning clinical care, public health, translational research, consumer engagement, and life sciences innovation. We propose an expanded theorem: A learning biomedical ecosystem that continuously optimizes human-AI collaboration will outperform humans or AI alone.

This evolution builds directly upon Friedman's original theorem, reaffirming its human-centered foundation, while incorporating AI-enabled computation, adaptive learning, and systems-level integration across the modern biomedical enterprise.