JMIR Publications

ABSTRACT

The integration of medical open databases with artificial intelligence (AI) technologies marks a transformative era in biomedical research and healthcare innovation. Over the past 25 years, initiatives like PhysioNet have revolutionized data access, fostering unprecedented levels of collaboration and accelerating medical discoveries. This rise of medical open databases presents challenges, particularly in harmonizing research enablement with patient confidentiality. In response, privacy laws such as Health Insurance Portability and Accountability Act have been established, and privacy-enhancing technologies (PETs) have been adopted to maintain this delicate balance. PETs, including differential privacy, secure multiparty computation, and notably, federated learning, have become instrumental in safeguarding personal health information. Federated learning, in particular, represents a significant advancement by enabling the development and training of AI models on decentralized data. In Taiwan, significant strides have been made in aligning with these global data-sharing and privacy standards. We have actively promoted the sharing of medical data through the development of dynamic consent systems. These systems enable individuals to control and adjust their data-sharing preferences, ensuring transparency and continuity of consent in the ever-evolving landscape of digital health. Despite the challenges associated with privacy protections, the benefits, including improved diagnostics and treatment, are substantial. The availability of open databases has notably accelerated AI research, leading to significant advancements in medical diagnostics and treatments. As the landscape of healthcare research continues to evolve with open science and federated learning, the role of medical open databases remains crucial in shaping the future of medicine, promising enhanced patient outcomes and fostering a global research community committed to ethical integrity and privacy.