Accelerating artificial intelligence: How federated learning can protect privacy, facilitate collaboration, and improve outcomes

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,...

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Veröffentlicht in:Health informatics journal Jg. 29; H. 4; S. 14604582231207744
Hauptverfasser: Patel, Malhar, Dayan, Ittai, Fishman, Elliot K, Flores, Mona, Gilbert, Fiona J, Guindy, Michal, Koay, Eugene J, Rosenthal, Michael, Roth, Holger R, Linguraru, Marius G
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London, England SAGE Publications 01.10.2023
SAGE PUBLICATIONS, INC
Schlagworte:
Artificial Intelligence
Cancer
Challenges
Collaboration
COVID-19
Data
Emergency services
Humans
Innovations
Learning
Oxygen
Pancreatic cancer
Pancreatic Neoplasms
Patients
Privacy
Radiology
data security and confidentiality
databases and data mining
collaborative work practices and IT
privacy
clinical decision-making
decision-support systems
machine learning
IT healthcare evaluation
cloud computing
medical imaging
ISSN:1460-4582, 1741-2811, 1741-2811
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Zusammenfassung: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.
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ISSN:1460-4582
1741-2811
1741-2811
DOI:10.1177/14604582231207744