AI and Digital Health Care:Managing Ethical and Legal Risk During Pandemic Outbreaks in the EU and Taiwan

Abstract

Digital technologies are fundamentally reshaping healthcare delivery — generating both transformative opportunities and complex regulatory challenges for governments, providers, and patients. The COVID-19 pandemic served as a critical stress test for digital health infrastructures worldwide, exposing persistent gaps between technological capability and governance readiness.<br/>This report examines the deployment of artificial intelligence and digital health technologies in pandemic contexts, offering a comparative analysis of two jurisdictions that have adopted markedly different — yet equally instructive — governance approaches: the European Union and Taiwan.<br/>Drawing on primary regulatory texts, case law, academic literature, and policy documentation, the report analyzes the EU's layered regulatory architecture, encompassing the General Data Protection Regulation (GDPR), the EU AI Act (Regulation EU 2024/1689), the Medical Devices Regulation (MDR), and the European Health Data Space (EHDS) Regulation. This framework is compared with Taiwan's evolving system, anchored in the Personal Data Protection Act (PDPA) and the National Health Insurance (NHI) infrastructure — one of the world's most comprehensive integrated health data ecosystems.<br/>The analysis reveals a fundamental tension between regulatory models. The EU prioritises a comprehensive, rights-based framework emphasising data minimisation, purpose limitation, and individual autonomy. Taiwan, by contrast, has leveraged a pragmatic, data-integrated approach that proved operationally superior during acute public health emergencies — though at the cost of broader surveillance capacity and reduced individual control over health data. Neither model is optimal in isolation: EU rigidity can impede legitimate crisis response, while Taiwan's efficiency-first orientation raises unresolved concerns about accountability and consent.<br/>The report identifies three structural convergences that could inform a shared governance path: interoperable data standards grounded in FAIR principles; risk-tiered AI oversight calibrated to clinical and public health contexts; and bilateral data transfer mechanisms compatible with GDPR adequacy requirements. Building on these convergences, the report develops a set of concrete recommendations for a converged governance model applicable to future pandemic preparedness, responsible AI deployment in healthcare settings, and the development of cross-border data-sharing arrangements between Taiwan and the EU.

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