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Updated: Apr 28, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Extending the Fundamental Theorem of Biomedical Informatics for the AI era.

Philip R O Payne¹, Jonathan H Chen², Christopher A Longhurst³

¹Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis School of Medicine, and Center for Health AI, WashU Medicine and BJC Health, St. Louis, Missouri, United States.

Journal of the American Medical Informatics Association : JAMIA

|April 26, 2026

Summary

Charles Friedman's Fundamental Theorem of Biomedical Informatics is updated for the AI era. A learning biomedical ecosystem optimizing human-AI collaboration outperforms humans or AI alone, extending the theorem's reach.

Keywords:

artificial intelligence attitudes health knowledge medical informatics practice systems analysis

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Last Updated: Apr 28, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Biomedical Informatics
Artificial Intelligence
Sociotechnical Systems

Background:

Friedman's Fundamental Theorem posits human-information resource partnership enhances performance.
Advances in AI and data infrastructure have expanded informatics across health and life sciences.
The theorem's importance is amplified, necessitating its expansion for current needs.

Purpose of the Study:

Reassess and extend the Fundamental Theorem for the AI era.
Preserve conceptual strength while broadening applicability in a complex biomedical ecosystem.
Adapt the theorem for contemporary informatics and AI integration.

Main Methods:

Synthesize empirical evidence and sociotechnical theory.
Contextualize the theorem within human-AI collaboration, learning health systems, and AI governance.
Utilize systems science for a comprehensive framework.

Main Results:

Propose shifting the unit of analysis from individuals/tools to adaptive sociotechnical systems.
Introduce an expanded theorem: A learning biomedical ecosystem optimizing human-AI collaboration surpasses individual or AI performance.
Highlight the theorem's applicability across clinical care, public health, research, and life sciences.

Conclusions:

The evolution reaffirms the theorem's human-centered foundation.
Incorporate AI-enabled computation and adaptive learning.
Emphasize systems-level integration across the modern biomedical enterprise.