Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions
TL;DR
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.
Abstract
Machine learning (ML) is transforming cancer research and care by enabling analysis of complex, high-dimensional datasets spanning genomics, transcriptomics, proteomics, imaging, and clinical records. By improving risk stratification, accelerating detection and diagnosis, and supporting treatment selection, ML has the potential to enhance survival outcomes while increasing efficiency across oncology workflows. This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches. We highlight major application areas including early cancer detection, tumor classification, molecular subtyping, biomarker discovery, prognosis estimation, multi-omics integration, computational pathology, pharmacogenomics, and clinical decision support. We also summarize commonly used datasets, discuss the importance of interpretability for clinical trust, and outline barriers to translation such as data heterogeneity, bias, and regulatory constraints. Finally, we describe future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology. This review is intended for cancer researchers, clinicians, and data scientists seeking a practical overview of ML methods, opportunities, and translational considerations in oncology.