🤖 AI Summary
Current medical AI systems often operate as isolated models, lacking accountability and the capacity for continuous evolution. This work proposes Clinical Harness, a runtime governance architecture that introduces the novel concept of “clinical AI skills” to establish a governable ecosystem unifying knowledge-driven, data-driven, and physics-enhanced AI capabilities. The framework enables registration, orchestration, safeguarding, and monitoring of these skills, facilitating coordinated clinical support across the entire care continuum. Using osteoporosis as a case study, the authors demonstrate that diverse types of AI skills can effectively collaborate under runtime governance to deliver comprehensive, end-to-end patient care.
📝 Abstract
Medical AI remains organized around isolated models, whereas clinical care requires accountable capabilities that persist across time. We propose clinical AI skills and the Clinical Harness: a runtime governance architecture for registering, orchestrating, guarding and monitoring AI-enabled clinical capabilities. Using osteoporosis as an exemplar, we show how knowledge-driven, data-driven and physics-enhanced skills can support lifecycle care under runtime governance.