Artificial intelligence in oncology: linking biological discovery to clinical utility.

backgroundArtificial 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.

methodsWe 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.

resultsThe 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: Multi-omics, single-cell, spatial transcriptomics, graph-based, and perturbation models increasingly yield falsifiable hypotheses regarding tumor microenvironments, regulatory networks, and drug vulnerabilities; most, however, lack upstream functional validation. Therapeutic Decision Support: Applications in treatment response, surgical assistance, toxicity monitoring, and clinical large language models (LLMs) remain limited by cohort heterogeneity, temporal drift, unstandardized endpoints, and a lack of evidence that model-guided care alters patient outcomes.

conclusionAI applications must be evaluated using evidence matched directly to their specific clinical or biological claims: external validation for transportability, calibration/decision analysis for utility, prospective workflow studies for operational value, randomized trials for patient outcome benefits, and perturbation experiments for biological mechanism. This claim-matched framework clarifies the boundary between promising computational models and clinically or mechanistically credible oncology, establishing a roadmap for reliable integration into precision cancer care.