This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches, and describes future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology.
Kanishk Yadav, Taneesha Gupta· Journal of the Egyptian Nati...· 0 citations
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