Artificial intelligence in oncology: linking biological discovery to clinical utility
Abstract
Background: Artificial intelligence (AI) has expanded rapidly across the oncology continuum-spanning early detection, histopathology, molecular profiling, treatment selection, drug discovery, toxicity surveillance, and survivorship care. However, these varied applications occupy fundamentally different stages of clinical and biological translation.
Methods: We conducted a critical narrative review searching PubMed/MEDLINE, Europe PMC, IEEE Xplore, arXiv, and ClinicalTrials.gov (updated to July 21, 2026). From these sources, an evidence map of 202 publications (including 182 original studies) was assembled based on study design, prospective or external validation, mechanistic rigor, clinical utility, and representation across the cancer continuum.
Results: The primary synthesis demonstrates evidence gradient rather than uniform translation across oncology: Detection & Imaging: Select mammography and colonoscopy tools are supported by randomized controlled trials demonstrating workflow efficiency and detection gains. Conversely, negative pragmatic trials highlight that robust technical accuracy does not automatically translate into improved diagnostic pathways.
Pathology: Large self-supervised and vision-language foundational models exhibit strong cross-task and cross-institutional transferability. However, prospective real-world deployment remains rare, and reporting on calibration, subgroup performance, and data provenance is inconsistent. Molecular & Systems Biology:…
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