Federated Learning: Revolutionizing Secure Data Sharing in Healthcare
MobiHealthNews
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Contributed by: Kate Gamble
Summary
During the HIMSS AI and Cybersecurity Virtual Forum, Dr. Xiaoqian Jiang emphasized the complexities of data sharing in healthcare, particularly regarding privacy challenges under a consortium model. He warned that merely anonymizing data does not sufficiently protect individuals from re-identification through available demographic information, especially concerning genomic data linked through public databases. Jiang proposed federated learning as a promising solution, enabling collaborative AI model training across diverse datasets while maintaining patient confidentiality. This approach, exemplified by UTHealth Houston's workflow manager, highlights the potential for technological innovations to enhance data privacy in healthcare amidst growing collaboration demands.
