Medicine for Artificial Intelligence (MAI): Applying a Medical Framework to AI Anomalies

Abstract

Background: AI systems often exhibit anomalies such as hallucinations, misalignment, or mode collapse. However, current terminology is inconsistent, lacking a standardized framework for diagnosis and intervention. Objective: To establish Medicine for Artificial Intelligence (MAI)-a novel interdisciplinary framework that conceptualizes AI anomalies as "diseases" and enables the structured diagnosis, classification, and treatment analogous to clinical medicine. Methods: Inspired by human medical nosologies, key concepts (disease, symptom, diagnosis, treatment) were defined and a prototype diagnostic manual, DSA-1, categorizing 45 AI disorders into 9 chapters based on real-world incidents, were constructed. Results: DSA-1 enables differential diagnosis among failure modes such as hallucinations due to memory error, retrieval failure, or misalignment. Case studies and diagnostic algorithms illustrated its clinicalstyle application. Conclusion: MAI provides a systematic, reproducible method for managing AI anomalies, bridging engineering and medicine. This paradigm shift allows anomaly data to be accumulated, classified, and acted upon, potentially enhancing safety, transparency, and trust in AI systems.

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