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Accelerating artificial intelligence: How federated learning can protect privacy, facilitate collaboration, and
Malhar Patel1, Ittai Dayan1, Elliot K Fishman2
1Rhino Health, Boston, MA, USA.
Health Informatics Journal
|October 21, 2023
Summary
Federated learning (FL) overcomes data-sharing obstacles in cross-institutional research, enabling robust artificial intelligence model development. This approach trains models locally, enhancing privacy and collaboration for improved outcomes.
Area of Science:
- Artificial Intelligence
- Medical Informatics
- Collaborative Research
Background:
- Cross-institutional collaborations are hindered by data-sharing challenges, impeding AI innovation.
- Federated learning (FL) offers a solution by enabling decentralized model training without direct data sharing.
Purpose of the Study:
- To describe key insights from a panel discussion on accelerating AI through federated learning.
- To highlight the potential of FL in protecting privacy, facilitating collaboration, and improving healthcare outcomes.
Main Methods:
- Discussion of insights from researchers involved in the EXAM study and the National Cancer Institute's Early Detection Research Network (EDRN).
- Explanation of the federated learning paradigm: models trained locally, weights aggregated centrally.
- Presentation of real-world applications, including a COVID-19 patient oxygen requirement model and a pancreatic cancer early detection initiative.
Main Results:
- Federated learning enables multi-institutional AI development by addressing data privacy and access concerns.
- Successful application of FL in diverse medical domains, demonstrating its versatility and effectiveness.
- Panelists discussed the motivations, challenges, and future directions for federated learning implementation.
Conclusions:
- Federated learning is a transformative approach for collaborative AI research in healthcare.
- FL facilitates the creation of powerful AI models by leveraging diverse datasets while preserving patient privacy.
- Continued development and adoption of FL are crucial for advancing medical AI and improving patient outcomes.
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