The S.A.M. Model: A Framework for Safe, Supervised AI‑Assisted Medicine
Year: 2026
Type: preprint
Abstract: The S.A.M. Model (Supervised Autonomous Medicine) proposes a safety‑first framework for integrating artificial intelligence and robotic instrumentation into surgical practice without removing human oversight. The system combines large‑scale virtual simulation, post‑mortem learning, similarity‑based pattern recognition, and real‑time multi‑layer monitoring to create a fault‑tolerant, surgeon‑supervised medical environment. AI functions exclusively as a predictive monitor, robots provide precision execution, and surgeons retain full authority and responsibility at all times. The model outlines a complete training pipeline, operational structure, safety architecture, and economic rationale for a 24/7, high‑consistency surgical system designed to reduce human error while preserving ethical and clinical governance.
Author: willoughby, samuel james
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7161572523
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