The role of artificial intelligence in cancer epidemiology:Challenges and opportunitiesOA

Artificial intelligence(AI)is rapidly reshaping cancer research,but high technical performance alone is not sufficient for cancer epidemiology,which requires population representativeness,measurement validity,causal reasoning,and demonstrable population-level benefit.This review evaluates AI applications across the cancer epidemiology continuum,from surveillance and data infrastructure through primary prevention,screening and early detection,prognosis,comparative effectiveness,and survivorship.We use a diagnostic matrix that crosses three epidemiologic pillars(study population,measurement,and inference)with six stages of the AI lifecycle,from problem definition to post-deployment monitoring.The current evidence base is promising but uneven.Natural language processing and large language models can improve cancer registration,yet cross-registry transportability and confidentiality safeguards remain incompletely documented.In etiologic research,AI-discovered associations often lack formal causal evaluation.In screening,randomized program-level evidence is the strongest for mammography and colonoscopy,but endpoints remain largely intermediate,and no completed AI trial has shown cancer-specific mortality reduction.In prognostic modeling,most published models remain externally unvalidated beyond their development institutions.These limitations are compounded by an equity gap:Training data are concentrated in high-income and Europeandescent populations,while many regions with rapidly growing cancer burdens are still underrepresented.We propose minimum deployment standards for cancer AI,including multidimensional external validation,calibration assessment,decision curve analysis,equity-stratified reporting,privacy and consent governance,and algorithm vigilance systems for post-deployment monitoring.Realizing the potential of AI in cancer epidemiology will require not only accurate algorithms but also epidemiologic standards that make the population validity,clinical utility,and governance of such algorithms auditable.