The economic and clinical impact of an AI-assisted tool for colonoscopy to support diagnosis of colorectal cancer in symptomatic patients in England

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality in the UK. Early detection and removal of adenomatous polyps are essential to improving outcomes. Artificial intelligence-assisted colonoscopy tools, such as GI Genius (GIG), may enhance adenoma detection rates (ADR) and reduce the likelihood of missed lesions. A patient-level simulation cost-effectiveness model was developed to estimate the economic and clinical impact of GIG-assisted colonoscopy compared with standard colonoscopy alone in a symptomatic for suspected CRC population in England. The model adopted a National Health Service and Personal Social Services perspective, with a lifetime time horizon. Key model inputs were informed by the NAIAD trial, which evaluated the impact of GIG-assisted colonoscopy on ADR, compared with standard colonoscopy. Base case and probabilistic sensitivity analysis (PSA) were conducted to estimate the incremental cost-effectiveness ratio and the probability of cost effectiveness. Base case results suggest that GIG is a cost-effective intervention, associated with per-person cost savings of £69 and quality-adjusted life year (QALY) gains of 0.046. This was primarily due to increased ADR and subsequent reduced CRC incidence. Scenario analyses testing uncertainty around the sensitivity of colonoscopy, clinical outcomes for those initially undiagnosed, differences in outcomes across experienced and non-experienced endoscopists, and the total costs of GIG produced findings that were consistent with the base case and did not materially change the estimated costs or QALYs. PSA results indicate that GIG is cost effective in 53% of simulations at the £20,000 per QALY gained threshold. This uncertainty is attributable to the marginal change in QALY outcomes between arms of the model, meaning that minor fluctuations in QALY outcomes between PSA iterations lead to large variations in the ICER. The results highlight that GIG is a cost-saving intervention that can improve health outcomes by increasing ADRs. Scenario analyses broadly support the base case findings, suggesting that the conclusions were generally consistent across the alternative assumptions explored. PSA results highlighted considerable decision uncertainty, driven by the small incremental differences in QALYs. PSA results highlight considerable decision uncertainty, driven by the small incremental differences in QALYs.