Health economics evaluation of artificial intelligence in the field of oncology: a scoping review

Abstract Background The ageing and growing population, rising cancer rate, and increased healthcare demand drive higher medical costs. Integrating Artificial Intelligence (AI) into oncology revolutionizes cancer diagnosis, treatment, and management, promising enhanced efficiency and precision. Despite potential for healthcare cost savings from implementing AI system in healthcare, its successful integration depends on stakeholder acceptance, particularly physician adoption and integration into routine clinical practice. Objectives This scoping review aimed to systematically assess recent literature on health economic evaluations (HEEs) of artificial AI applications in oncology and explore the economic risks and benefits of integrating AI systems into oncology. Methodology A scoping review was conducted using PRISMA-ScR guidelines across databases, including PubMed, Scopus, and Google Scholar for articles published between January 1, 2019, and June 30, 2024, in English. Eligible studies focused on the economic aspects of AI applications in oncology, including cost-effectiveness analyses, budget impact studies, and evaluations of economic benefits in clinical practice. Two independent reviewers used CHEERS-AI and Philips checklists for data extraction, quality assessment and analysis. Results Out of 870 studies identified,12 studies were selected which focused on colorectal (5), breast (2), lung (2), cervical (1), and prostate (1) cancers. Most emphasis is on early-stage care (N = 10,83%) and cost-effectiveness analysis (N = 9,75%) was predominant along with Markov model being the most common approach. The studies used a healthcare system perspective (N = 6,50%) and examined AI's cost-effectiveness in medical imaging (N = 5,42%) and biomarkers (N = 4,33%). Time horizons varied from less than a year to a lifetime, with most applying a 3% discount rate. AI demonstrated economic benefits, improved diagnostic sensitivity,potential cost reduction, workflow efficiency, and treatment optimization while presenting risks like reimbursement challenges, data security concerns, and potential error costs. Conclusion The scoping review highlights the necessity for thorough health economic evaluations of AI integration in oncology. Although AI technologies are cost-effective, there is a gap in user perspectives and considerations of health equity, regulation, and ethics. To maximize its benefits, future research should include a comprehensive economic evaluation of a more diverse population for the effective adoption of AI into the healthcare system.