arXiv:2606.26494v1 Announce Type: new 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.
The proliferation of AI models in healthcare necessitates robust governance frameworks to ensure safety, accountability, and clinical utility, moving beyond isolated proofs of concept.
This development addresses the critical challenge of integrating AI into regulated clinical environments, enabling reliable and persistent AI capabilities vital for patient care and regulatory approval.
The focus shifts from siloed medical AI models to governable, orchestratable AI skills within a clinical context, impacting how AI is developed, deployed, and monitored in healthcare.
- · Healthcare IT providers
- · Medical AI developers
- · Hospitals and clinics
- · Patients
- · Makers of unregulated medical AI solutions
- · Healthcare systems slow to adopt AI governance
Improved safety and reliability of AI applications in clinical settings.
Accelerated adoption and broader integration of AI across various medical specialities due to increased trust and accountability.
The emergence of new regulatory paradigms and standards specifically for governable, persistent medical AI systems.
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Read at arXiv cs.AI