Cost-effectiveness and environmental impact of artificial intelligence-assisted colonoscopy with the resect-and-discard strategy: a microsimulation study

Background Artificial intelligence (AI)-assisted colonoscopy reduces costs in colorectal cancer screening, but its incremental value relative to non-AI resect-and-discard (RD) strategies for diminutive polyps (≤ 5 mm) remain unclear. We aimed to compare the clinical, economic, and environmental outcomes of AI-assisted and non-AI RD strategies with standard of care (SoC). Methods In our Markov model simulating 6 million individuals aged ≥45 years undergoing screening colonoscopy, 4 strategies were evaluated: SoC, non-AI RD, CADe-assisted RD (CADe-RD), and CADe+CADx-assisted RD (CADe+CADx-RD). The primary outcome was total quality-adjusted life years (QALYs), with secondary outcomes including cost and carbon dioxide (CO₂) emissions. Results Non-AI RD achieved the lowest total cost ($36.6B) and carbon emissions (0.90 million kg CO₂), while yielding the highest QALYs (51.37 million), although differences in QALYs across strategies were small (