Trust and Reliability of AI Systems in Early Cancer Diagnostics: A Narrative Review

Artificial intelligence (AI) tools are now embedded in many early cancer diagnostic workflows, with selected systems matching or surpassing human readers in mammography, low-dose computed tomography, colonoscopy, dermoscopy, and digital pathology. Whether such tools can be safely scaled depends on more than headline accuracy. Clinicians, patients, regulators, and health systems must be able to trust them and rely on their behaviour across diverse populations and over time. This narrative review draws together evidence from PubMed and PubMed Central published between 2018 and 2026, with priority given to randomised trials, large prospective studies, systematic reviews, and authoritative regulatory analyses. We examine six interrelated dimensions of trust and reliability: diagnostic performance against clinical reference standards; external validity and generalisability across populations, scanners, and time; algorithmic bias and health equity; transparency through explainable AI methods; human–AI interaction, including automation bias; and the evolving regulatory landscape under the United States Food and Drug Administration and the European Union AI Act. Recent evidence shows that well-validated AI can raise cancer detection rates and reduce reader workload in real-world screening. Reliability, however, is constrained by limited external validation, demographic under-representation, opaque decision-making, and uneven post-market surveillance. Trust in this domain is not an intrinsic property of any single model. It emerges from rigorous validation, transparent reporting, equitable performance, regulatory oversight, and meaningful human oversight maintained throughout the lifecycle of every deployed tool.