Federated Learning in Precision Oncology: Bridging Multi-Institutional Artificial Intelligence and Clinical Implementation

Abstract

Precision oncology, an evolving branch of medical science, is increasingly dependent on the application of AI techniques to aid accurate diagnosis, molecular profiling, proper therapy selection, and outcome prediction in cancer patients. However, several problems such as fragmented health care data, institutional barriers, and privacy restrictions related to the use of patient data continue to impede the development of clinically applicable and generalizable AI models. Federated learning (FL) is a newly developed paradigm that enables collaborative creation of AI models without compromising data privacy. In this commentary, we describe how FL is evolving into an essential component across all stages of the precision oncology process, including areas such as radiomics, digital pathology, genomics, multi-omics analysis, molecular tumor board decision-making, patient matching in clinical trials, and development of multimodal AI algorithms. We also address the issue of transitioning from a model with strong technical performance to one that demonstrates good clinical performance by considering aspects of prospective validation, integration into workflows, model interpretability, cybersecurity, governance, and regulation. FL should be thought of as crucial infrastructure rather than just an efficient machine-learning technique for privacy-sensitive environments.