隨著人工智慧（AI）與數位健康技術快速發展，智慧醫療已成為慢性病管理的重要發展方向。然而，AI 導入醫療場域不僅涉及技術應用，更牽涉多元行動者之協作與治理問題。本研究以「生態系統即結構（ecosystem-as-structure）」觀點為基礎，探討不同智慧醫療生態系統結構下之風險分布與治理機制。
本研究採多重個案研究法，以台灣兩個糖尿病管理案例為研究對象：由健保署主導之 AI-on-DM 計畫，以及慧康生活科技發展之 Health2Sync（智抗糖）平台。研究從活動、行動者、位置與連結四個構面分析生態系統結構，並進一步探討其所產生之結構性風險、治理與合作風險，以及 AI 特有風險。
研究發現，政府主導與平台主導之智慧醫療生態系統，在風險分布與治理邏輯上存在明顯差異。AI-on-DM 較容易面臨跨機構協調、資料治理與採用鏈風險；Health2Sync 則較偏向平台治理、商業依賴與資料控制問題。此外，兩者皆面臨演算法偏誤、模型可解釋性與臨床信任等 AI 治理挑戰。
本研究從生態系統結構視角重新理解智慧醫療中的風險治理問題，並提出適用於 AI 智慧醫療情境之分析架構。作為未來智慧醫療治理與跨部門協作之參考。

With the rapid development of artificial intelligence (AI) and digital health technologies, smart healthcare has become an important approach to chronic disease management. However, the adoption of AI in healthcare involves not only technological applications but also collaboration and governance issues among multiple stakeholders. Drawing on the ecosystem-as-structure perspective, this study examines how different smart healthcare ecosystem structures shape risk distribution and governance mechanisms.
This study adopts a multiple-case study approach and investigates two diabetes management cases in Taiwan: the government-led AI-on-DM project initiated by the National Health Insurance Administration and the platform-based Health2Sync developed by H2 Inc. The study analyzes ecosystem structures through four dimensions—activities, actors, positions, and links—and further explores structural risks, governance and collaboration risks, and AI-specific risks within smart healthcare ecosystems.
The findings suggest that government-led and platform-led smart healthcare ecosystems exhibit distinct patterns of risk distribution and governance logic. The AI-on-DM ecosystem is more likely to encounter cross-institutional coordination, data governance, and adoption-chain risks, whereas Health2Sync faces greater challenges related to platform governance, commercial dependency, and data control. In addition, both cases confront AI-related governance issues, including algorithmic bias, lack of explainability, and clinical trust.
This study contributes to the understanding of risk governance in smart healthcare from an ecosystem structure perspective and proposes an analytical framework applicable to AI-enabled healthcare ecosystems.

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