How to Cross the Data Governance Trust Gap in Medical AI: Five Pillars of a Reliable Healthcare System

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The forum highlighted that the adoption of medical AI faces structural issues beyond mere technology, such as achieving a positive Return on Investment (ROI) and seamless integration into clinical settings. A major hurdle identified is the 'data silo' problem, where different hospitals use disparate IT systems, complicating patient data sharing and AI deployment. To address this, building a centralized data platform based on international standards like HL7 FHIR is crucial. Furthermore, the concept of 'Trustworthy AI' was emphasized, requiring governance frameworks (referencing ISO 42001 or NIST) overseen by executive-level committees. This involves regular audits for model bias and AI hallucination, alongside establishing a mandatory 'human-in-the-loop' mechanism for final clinical decisions. Practically, the article stresses that systemic change requires simultaneous action across five dimensions: Finance, Payment, Organization/Procurement, Regulation, and Behavior Change. Successful implementation examples showed that value was created not by complex algorithms alone, but by integrating IoT sensors and REST APIs into existing nursing workflows (e.g., monitoring IV drips or urine bags), achieving immediate clinical efficiency gains. Ultimately, the commercial sustainability of AI depends on reengineering clinical processes to demonstrate tangible improvements in quality and efficiency, thereby convincing payment systems (like national health insurance) to adjust reimbursement strategies.