AI for screening in healthcare: promise and challenges

Artificial intelligence (AI) is reshaping population screening, yet the translation from laboratory performance to population benefit remains limited. This narrative review describes current uses of AI across major screening pathways. Prospective trials in mammography demonstrate non‑inferior cancer detection with large reductions in radiologist workload. In diabetic retinopathy, the first FDA‑authorized autonomous system extends specialist‑level screening into primary care and improves uptake. During colonoscopy, real‑time computer vision improves adenoma detection without increasing removal of non‑neoplastic tissue. Emerging multimodal approaches, including transformer‑based and large language model-enabled systems, integrate images, clinical variables, and molecular signals and underpin multi‑cancer early detection tests. Despite these gains, three constraints currently limit impact: the base‑rate problem in low‑prevalence cohorts, which magnifies the burden of false positives; limited generalizability and potential bias across institutions and populations; and practical barriers in workflow, regulation, and trust. Opportunities ahead include foundation models pre‑trained on diverse data, uncertainty‑aware "decision referral," federated learning, larger representative datasets, and prospective trials that track interval cancers, stage shift, and cost‑effectiveness. The overarching conclusion is cautious optimism: when validated and invisibly integrated, AI augments physicians, expands access, and improves efficiency; realizing durable public‑health benefits will depend on equity‑focused design, rigorous evaluation, and sustained human oversight.