日本和台灣醫療人工智慧治理和給付框架的比較研究

Abstract

全球衛生系統正面臨人口快速老化、慢性病負擔加重和醫療支出不斷攀升帶來的日益嚴峻的壓力。這些結構性挑戰在日本和台灣尤為突出，這兩個地區必須在勞動力萎縮和醫療服務需求不斷增長的情況下維持全民健康覆蓋。在此背景下，人工智慧（AI）和醫療器材軟體（SaMD）已成為提高效率、改善臨床品質和支援永續醫療服務的重要工具。然而，兩國在人工智慧醫療技術的監管、審批和報銷方面存在顯著差異。 本研究對日本和台灣的醫療人工智慧和醫療器材軟體的治理結構、監管框架、資料基礎設施和報銷機制進行了比較分析。透過對政策文件、政府指南、學術文獻和機構報告進行基於文件的定性回顧，本研究考察了兩國如何管理人工智慧的生命週期——從開發和驗證到實施和上市後監管。本研究重點在於制度化程度、資料標準化、衛生技術評估（HTA）整合以及與HL7 FHIR、GA4GH和歐盟人工智慧法案等國際標準的契合度。 研究結果揭示了根本性的結構差異。台灣採用集中式、國家主導的治理模式，並擁有完善的製度安排，包括國家級GPU集群、強制採用FHIR標準、聯邦學習基礎設施、臨床人工智慧驗證中心以及專門負責公平性、透明度和生命週期監控的治理機構。相較之下，日本則依賴以自願性指南為核心、資料系統分散且依賴機構的治理實踐的去中心化模式。儘管日本透過《藥品和醫療器材法案》（PMD Act）和IDATEN系統展現了卓越的技術實力和健全的監管審查機制，但資料互通性、外部驗證和上市後監控方面的局限性阻礙了其在全國範圍內的推廣應用。 總之，台灣的基礎設施-治理整合模式為安全、公平且可擴展的人工智慧實施提供了堅實的政策基礎。日本可能需要進一步優先考慮資料標準的製度化、加強外部驗證、開發國家一體化資料平台以及完善生命週期治理，以更好地支援安全有效的AI部署。

Health systems worldwide face mounting pressures from rapid population aging, increasing chronic disease burdens, and escalating medical expenditures. These structural challenges are particularly acute in Japan and Taiwan, where universal health coverage must be sustained despite shrinking workforces and rising demand for medical services. Against this backdrop, artificial intelligence (AI) and Software as a Medical Device (SaMD) have emerged as essential tools for enhancing efficiency, improving clinical quality, and supporting sustainable healthcare delivery. However, the pathways through which AI-enabled medical technologies are governed, approved, and reimbursed differ substantially between the two countries. This study conducts a comparative analysis of Japan and Taiwan’s governance structures, regulatory frameworks, data infrastructures, and reimbursement mechanisms for healthcare AI and SaMD. Using a document-based qualitative review of policy papers, governmental guidelines, academic literature, and institutional reports, the study examines how each country manages the AI lifecycle—from development and validation to implementation and post-market oversight. Particular focus is placed on the degree of institutionalization, data standardization, HTA integration, and alignment with international standards such as HL7 FHIR, GA4GH, and the EU AI Act. The findings reveal a fundamental structural divergence. Taiwan employs a centralized, state-led governance model with strong institutional arrangements, including national GPU clusters, mandatory FHIR adoption, federated learning infrastructure, clinical AI validation centers, and dedicated governance bodies for fairness, transparency, and lifecycle monitoring. In contrast, Japan relies on a decentralized model centered on voluntary guidelines, fragmented data systems, and institution-dependent governance practices. While Japan demonstrates technological excellence and robust regulatory review via the PMD Act and IDATEN system, limitations in data interoperability, external validation, and post-market monitoring impede nationwide scalability. In conclusion, Taiwan’s integrated infrastructure–governance model provides a strong policy foundation for safe, equitable, and scalable AI implementation. Japan may need to further prioritize the institutionalization of data standards, enhancement of external validation, development of integrated national data platforms, and refinement of lifecycle governance to better support safe and effective AI deployment.