Research on the Economic Value and Risk Governance of AI-Assisted Medical Diagnosis

Abstract

AI-assisted medical diagnosis is becoming an important part of digital healthcare. It can help doctors read images, identify possible disease signals, and support earlier clinical decisions. This paper studies the economic value and risk governance of AI-assisted medical diagnosis from the perspective of healthcare economics and hospital management. The study uses literature analysis and normative analysis. It reviews 12 published studies and reports on medical artificial intelligence, clinical validation, economic evaluation, and ethical governance. The findings show that AI-assisted diagnosis may improve diagnostic efficiency, reduce repeated work, support cost control, and help allocate medical resources more evenly. At the same time, its value depends on data quality, clinical validation, workflow integration, legal responsibility, and patient trust. The paper argues that AI should not replace doctors in diagnosis. It should work as a decision-support tool under human review. Hospitals and regulators should build clear rules for data use, performance testing, responsibility sharing, and long-term economic evaluation.