Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Assessing the Evolution and Influence of Medical Open Databases on Biomedical Research and Health Care Innovation: A 25-Year Perspective With a Focus on Privacy and Privacy-Enhancing Technologies
0
Zitationen
15
Autoren
2025
Jahr
Abstract
The integration of medical open databases with artificial intelligence (AI) technologies marks a transformative era in biomedical research and health care 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 the Health Insurance Portability and Accountability Act have been established, and privacy-enhancing technologies have been adopted to maintain this delicate balance. Privacy-enhancing technologies, including differential privacy, secure multiparty computation, and notably, federated learning (FL), have become instrumental in safeguarding personal health information. FL, 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 health care research continues to evolve with open science and FL, 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.
Ähnliche Arbeiten
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
2002 · 8.395 Zit.
Calibrating Noise to Sensitivity in Private Data Analysis
2006 · 6.872 Zit.
Deep Learning with Differential Privacy
2016 · 5.595 Zit.
Communication-Efficient Learning of Deep Networks from Decentralized\n Data
2016 · 5.591 Zit.
Large-Scale Machine Learning with Stochastic Gradient Descent
2010 · 5.564 Zit.
Autoren
Institutionen
- National Yang Ming Chiao Tung University(TW)
- Taipei Veterans General Hospital(TW)
- Kaohsiung Medical University(TW)
- Cornell University(US)
- Kaohsiung Medical University Chung-Ho Memorial Hospital(TW)
- National Taipei University of Nursing and Health Science(TW)
- Institute of Information Science, Academia Sinica(TW)
- Taichung Veterans General Hospital(TW)
- National Health Research Institutes(TW)