Paper Title
Federated Learning and Artificial Intelligence in Healthcare: A Privacy-Preserving Approach for Medical Data.
Keywords
- federated learning
- healthcare ai
- privacy-preserving computation
- deep learning
- predictive analytics
Research Impact Tools
Publication Info
Volume: 6 | Issue: 1 | Pages: 107-120
Published On
February, 2023
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Abstract
The integration of Artificial Intelligence (AI) into healthcare is unlocking unprecedented opportunities for improved diagnostics, personalized treatment, and predictive analytics. However, leveraging sensitive medical data at scale poses significant challenges due to stringent privacy regulations such as HIPAA and GDPR, fragmented data repositories, and growing concerns over healthcare data security. This paper introduces a novel Federated Learning (FL) framework that directly addresses these barriers by enabling collaborative AI model training across decentralized healthcare institutions—without transferring raw patient data. Through advanced techniques such as secure multiparty computation, differential privacy, and adaptive federated optimization, the proposed framework ensures robust privacy preservation, regulatory compliance, and scalability. Experimental results using real-world datasets (MIMIC-III and CheXpert) demonstrate that our FL framework achieves near-centralized model accuracy while significantly reducing data exposure risks. By offering a secure, privacy-aware, and regulation-aligned approach to AI in healthcare, this work lays the foundation for trustworthy, large-scale AI deployment across diverse clinical environments.
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