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This is a Literature Review study.

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September 18, 2025Open Access

Federated Learning for Multi-Institutional AI in Healthcare via Digital Pathology

Key Points

Federated learning enhances AI training without sharing sensitive patient data, paving the way for robust models.
Recent applications of federated learning in digital pathology indicate improved diagnostic outcomes and treatment predictions.
The review identifies technical, regulatory, and ethical hurdles that need to be overcome for effective implementation of federated learning.
Exploring future directions, the review emphasizes the necessity for standardization and validation of federated learning approaches.

Abstract

The integration of artificial intelligence (AI) in digital pathology has shown significant promise in advancing cancer diagnostics, grading, and treatment response prediction. However, widespread development and deployment of robust AI models face critical challenges due to data silos, privacy concerns, and the need for large-scale multi-institutional datasets. Federated Learning (FL) presents a transformative approach by enabling collaborative model training across hospitals without direct data sharing. In this review, we summarize recent developments in FL as applied to digital pathology, highlight pioneering use cases, and explore the technical, regulatory, and ethical hurdles. We discuss how FL can enable scalable, privacy-preserving AI models, and outline future directions for standardizing and validating FL-based approaches in clinical workflows.

Federated Learning for Multi-Institutional AI in Healthcare via Digital Pathology

Key Points

Abstract

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