Clinical Harness for Governable Medical AI Skill Ecosystems
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Abstract (arXiv)
Medical AI remains organized around isolated models, whereas care requires accountable capabilities that persist across time. We define clinical AI skills and propose the Clinical Harness, a runtime governance architecture that registers, orchestrates, constrains and monitors them. Using osteoporosis as an exemplar, we show how knowledge-driven, data-driven and physics-enhanced skills can support lifecycle care and provide a governed substrate for future medical agents.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 58 references that are in the map. First posted on arXiv 25 Jun 2026.
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11 of 58 references.
References · 11
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
- 2022Large Language Models Encode Clinical Knowledgecited by 1,807
- 2018Model Cards for Model Reportingcited by 1,068
- 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
- 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 551
- 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
- 2023FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcarecited by 59
- 2021Medical Imaging and Machine Learningcited by 12
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