Navigating AI and machine learning in cancer research: an end-to-end translational framework
TL;DR
This review provides a comprehensive pipeline for artificial intelligence/machine learning in cancer research, including preclinical research, clinical decision support, and real-world implementation, and critically examines multi-omics fusion architectures, regularization-based machine learning, batch-effect harmonization, explainable AI, and federated learning.
Abstract
Cancer is a complex and heterogeneous disease that is characterized by multi-level biological variability. Advances in high-throughput technologies have led to large-scale, high-dimensional data sets in cancer research, creating a pressing need for powerful computational techniques for successful data analysis. Current techniques may be inadequate for this purpose, thus underscoring the potential of artificial intelligence (AI) and machine learning (ML) for successful data analysis. This review provides a comprehensive pipeline for artificial intelligence/machine learning in cancer research, including preclinical research, clinical decision support, and real-world implementation. It emphasizes several important technologies, data integration, and implementation challenges. The review critically examines multi-omics fusion architectures, regularization-based machine learning, batch-effect harmonization, explainable AI, and federated learning, while addressing translational barriers including algorithmic bias, covariate drift, and regulatory asynchrony across Indian, US, and EU frameworks. Anchored by Decision Curve Analysis as a clinical utility benchmark, this narrative framework establishes that meaningful progress in precision oncology, early detection, and patient outcomes demands not only predictive accuracy but also externally validated, population-representative, and governance-compliant AI systems capable of sustained real-world oncology impact.