Model Medicine: A Clinical Framework for Understanding, Diagnosing, and Treating AI Models

• URL: http://arxiv.org/abs/2603.04722v1
• Date: Thu, 05 Mar 2026 01:49:29 GMT
• Title: Model Medicine: A Clinical Framework for Understanding, Diagnosing, and Treating AI Models
• Abstract summary: Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models.<n>This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research and the systematic clinical practice.
• Score: 4.358468367889626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions -- Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core--Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, comparison, localization, and predictive capability; (4) a five-layer diagnostic framework for comprehensive model assessment; and (5) clinical model sciences including the Model Temperament Index for behavioral profiling, Model Semiology for symptom description, and M-CARE for standardized case reporting. We additionally propose the Layered Core Hypothesis -- a biologically-inspired three-layer parameter architecture -- and a therapeutic framework connecting diagnosis to treatment.

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