ECONOMIC EVALUATION OF AI EARLY INTERVENTION VS. LATE-STAGE ONCOLOGY: A HEALTH-ECONOMIC SCOPING REVIEW

Cancer remains a leading cause of global mortality, imposing profound clinical and economic burdens on healthcare systems worldwide. The prevailing paradigm of late-stage oncology is characterised by exponential healthcare costs, intensive multimodal therapies, and severe financial toxicity for patients. Artificial Intelligence (AI) and its subsets, such as Machine Learning (ML), promise to disrupt this unsustainable trajectory by enabling earlier detection, optimising clinical triage, and personalising treatment planning. This article is a scoping review structured around a formal health-economic framework, synthesising evidence retrieved from PubMed, Scopus, and the Cochrane Library and reported in accordance with the PRISMA-ScR extension. It evaluates the comparative economic realities of implementing AI-driven early interventions against late-stage standard-of-care (SOC) oncology across payer, provider, and societal perspectives. While AI theoretically facilitates stage migration and reduces downstream costs associated with advanced disease, its integration is a highly capital-intensive process, introducing substantial upfront infrastructural costs and ongoing operational expenses, including the management of dataset shift and algorithmic bias. Furthermore, the downstream economic burden of false positives and overdiagnosis can weaken AI’s financial viability, particularly in complex-triage malignancies such as prostate cancer. Synthesising the current literature reveals a critical gap between modelled, in-silico cost-savings and rigorous Real-World Evidence (RWE). Ultimately, while AI holds transformative potential for reducing late-stage oncology burdens, its long-term cost-benefit profile is highly conditional upon equitable deployment, precise localised validation, appropriate clinical integration, and value-based reimbursement alignment.