Hamza Elkasaby, MBBCh1, Yara Hamdi, BA2, Ahmed Hassan, MBBCh3, Karim Ahmed, MBBCh4, Amr Mostafa, MBBCh1, Alaa Abdelsalhin, MBBCh5, Omar Sabry, MBBCh6, Moatazbellah Mustafa, MBBCh7, Mahmoud M. Elsayed, MD8 1Newgiza University, Zayed, Al Jizah, Egypt; 2Badr University in Cairo, Zayed, Al Jizah, Egypt; 3Columbia University Irving Medical Center, New York, NY; 4Newgiza University, Zayed, Al Jizah, Egypt; 5Misr University for Science and Technology, Rehab City, Al Qahirah, Egypt; 6New Giza University, New Cairo, Al Qahirah, Egypt; 7Modern University for Technology & Information, Rehab City, Al Qahirah, Egypt; 8MME Foundation, Monsoura, Ad Daqahliyah, Egypt Introduction: Colorectal cancer (CRC) remains a leading cause of cancer-related death, with colonoscopy being the gold standard for screening. Despite its efficacy, significant adenoma miss rates and cost inefficiencies persist. Artificial intelligence (AI)-assisted colonoscopy, particularly computer-aided detection (CADe), has demonstrated improved adenoma detection rates (ADR). However, the economic viability of AI integration into population-level CRC screening programs remains underexplored. To perform a meta-analysis of published economic evaluations of AI-assisted colonoscopy to determine its cost-effectiveness and impact on healthcare resource utilization in CRC screening programs. Methods: A systematic review was conducted using PubMed, Embase, and Cochrane (2015–2024) in accordance with PRISMA guidelines. Inclusion criteria were cost-effectiveness analyses, budget impact models, or health economic simulations evaluating AI-enhanced colonoscopy versus standard methods. Data were extracted on incremental cost-effectiveness ratio (ICER), quality-adjusted life years (QALYs), and cost per additional adenoma detected. Meta-analysis was performed using R (meta, metafor) and Python (pandas, statsmodels) to pool effect sizes and evaluate heterogeneity. Results: From 312 screened studies, 14 met inclusion criteria. AI-assisted colonoscopy was cost-effective in 12/14 models, with a pooled ICER of $14,730 per QALY gained (95% CI: $9,450–$20,870), well below the conventional $50,000 threshold. AI increased ADR by a mean of 10.2% (95% CI: 7.1–13.3%) and reduced the cost per advanced adenoma detected by 28%. Models incorporating downstream benefits (e.g., reduced interval cancers, fewer late-stage treatments) showed a net savings of $310–$580 per screened individual over a 10-year horizon. Sensitivity analyses identified ADR improvement and device cost as key drivers. However, heterogeneity in healthcare system models and reimbursement assumptions remained a limitation. Discussion: AI-assisted colonoscopy demonstrates robust cost-effectiveness and potential long-term savings in CRC screening programs. These findings support the strategic implementation of CADe systems to improve diagnostic yield and optimize healthcare resources. As a resident-led meta-analysis using real-world modeling data, this work underscores AI's role not only in clinical improvement but also in cost-containment—critical for national policy decisions.
Disclosures: Hamza Elkasaby indicated no relevant financial relationships. Yara Hamdi indicated no relevant financial relationships. Ahmed Hassan indicated no relevant financial relationships. Karim Ahmed indicated no relevant financial relationships. Amr Mostafa indicated no relevant financial relationships. Alaa Abdelsalhin indicated no relevant financial relationships. Omar Sabry indicated no relevant financial relationships. Moatazbellah Mustafa indicated no relevant financial relationships. Mahmoud M. Elsayed indicated no relevant financial relationships.
Hamza Elkasaby, MBBCh1, Yara Hamdi, BA2, Ahmed Hassan, MBBCh3, Karim Ahmed, MBBCh4, Amr Mostafa, MBBCh1, Alaa Abdelsalhin, MBBCh5, Omar Sabry, MBBCh6, Moatazbellah Mustafa, MBBCh7, Mahmoud M. Elsayed, MD8. P2620 - Meta-Analysis of Economic Evaluations of AI-Assisted Colonoscopy in CRC Screening Programs, ACG 2025 Annual Scientific Meeting Abstracts. Phoenix, AZ: American College of Gastroenterology.
