A Comprehensive Analysis of Personalized Medicine: Transforming Healthcare Privacy and Tailoring through Interoperability Standards and Federated Learning

Personalized medicine holds immense potential for transforming healthcare by tailoring treatments to individual patients, but its realization is hindered by privacy concerns and data fragmentation. This review explores the intersection of healthcare privacy and personalization, focusing on interoperability standards and federated learning. It discusses personalized medicine’s historical context, challenges, and evolution. The role of interoperability standards and privacy-preserving federated learning is examined. Privacy concerns, ethical considerations, and legal challenges in healthcare are addressed. The benefits of personalized medicine in improving outcomes, enhancing data security, and achieving cost-effectiveness are highlighted. Emerging technologies in healthcare data management are also discussed. The review underscores the potential of these technologies and the need for continued research and innovation to harmoniously integrate healthcare privacy and personalization.