Faculty, Staff and Student Publications
Publication Date
1-1-2023
Journal
Health Informatics Journal
DOI
10.1177/14604582231207744
PMID
37864543
Abstract
Cross-institution collaborations are constrained by data-sharing challenges. These challenges hamper innovation, particularly in artificial intelligence, where models require diverse data to ensure strong performance. Federated learning (FL) solves data-sharing challenges. In typical collaborations, data is sent to a central repository where models are trained. With FL, models are sent to participating sites, trained locally, and model weights aggregated to create a master model with improved performance. At the 2021 Radiology Society of North America's (RSNA) conference, a panel was conducted titled "Accelerating AI: How Federated Learning Can Protect Privacy, Facilitate Collaboration and Improve Outcomes." Two groups shared insights: researchers from the EXAM study (EMC CXR AI Model) and members of the National Cancer Institute's Early Detection Research Network's (EDRN) pancreatic cancer working group. EXAM brought together 20 institutions to create a model to predict oxygen requirements of patients seen in the emergency department with COVID-19 symptoms. The EDRN collaboration is focused on improving outcomes for pancreatic cancer patients through earlier detection. This paper describes major insights from the panel, including direct quotes. The panelists described the impetus for FL, the long-term potential vision of FL, challenges faced in FL, and the immediate path forward for FL.
Keywords
Humans, Artificial Intelligence, Privacy, Learning, Pancreatic Neoplasms
Published Open-Access
yes
Recommended Citation
Patel, Malhar; Dayan, Ittai; Fishman, Elliot K; et al., "Accelerating Artificial Intelligence: How Federated Learning Can Protect Privacy, Facilitate Collaboration, and Improve Outcomes" (2023). Faculty, Staff and Student Publications. 4600.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/4600
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Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons