AB110. The cost-effectiveness of artificial intelligence-assisted colonoscopy as a primary or secondary screening test in population-based colorectal cancer screening programmes: a Markov modelling study
Abstract

AB110. The cost-effectiveness of artificial intelligence-assisted colonoscopy as a primary or secondary screening test in population-based colorectal cancer screening programmes: a Markov modelling study

Martin C. S. Wong1,2,3,4,5, Junjie Huang1,5, Claire Chenwen Zhong1,5, Thomas Y. T. Lam6, Louis H. S. Lau7, Philip W. Y. Chiu8

1JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; 2The Chinese Academy of Medical Sciences and Peking Union Medical Colleges, Beijing, China; 3School of Public Health, Peking University, Beijing, China; 4School of Public Health, Fudan University, Shanghai, China; 5Centre for Health Education and Health Promotion, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; 6School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; 7Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; 8Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China

Background: The study provided the cost-effectiveness of using artificial intelligence (AI) colonoscopy in different population-based colorectal cancer (CRC) screening strategies. The study aimed to provide insights into the potential benefits and economic implications of incorporating AI colonoscopy into CRC screening programs.

Methods: To evaluate the cost-effectiveness of different population-based CRC screening strategies, including the use of AI-aided colonoscopy, by comparing incremental cost-effectiveness ratios (ICERs) and various outcome measures such as loss of cancer-related life-years, prevention of CRC cases, life-years saved, and total cost per life-year saved. Data from international literature and the government gazette were accessed to calculate relevant cost and performance estimates.

Results: The ICER of fecal immunochemical test (FIT) + colonoscopy, FIT + AI colonoscopy, colonoscopy, and AI colonoscopy was US$138,539, US$122,539, US$203,929, and US$180,444, respectively. When compared with FIT + colonoscopy, use of FIT + AI colonoscopy could lead to significantly smaller total loss of cancer-related life-years (5,355 vs. 5,327), a higher number and proportion of CRC cases prevented (120 vs. 132, 3.7% vs. 4.1%), more life-years saved (280 vs. 308), and lower total cost per life-year saved (US$944,008 vs. US$854,367). FIT + AI colonoscopy had the lowest ICER (US$122,539) and dominated across all other strategies (−US$36,462 vs. FIT + colonoscopy). When colonoscopy is adopted as a primary screening test, AI colonoscopy dominated conventional colonoscopy (ICER −39,040).

Conclusions: These findings show that FIT followed by AI colonoscopy is the most cost-effective strategy in population-based CRC screening programmes.

Keywords: Colorectal cancer (CRC); screening; artificial intelligence (AI); cost-effectiveness; life-years save

Footnote

Conflicts of Interest: M.C.S.W. is an honorary medical advisor of GenieBiome Ltd., SunRise, and BGI Health. He is an advisory committee member of Pfizer; an external expert of GlaxoSmithKline Limited; a member of the advisory board of AstraZeneca, and has been paid consultancy fees for providing advice on research. He has received grants from Pfizer, Merck Sharp & Dohme, AstraZeneca, GSK, and Moderna. The other authors have no conflicts of interest to declare.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the noncommercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

doi: 10.21037/jphe-26-ab110
Cite this abstract as: Wong MCS, Huang J, Zhong CC, Lam TYT, Lau LHS, Chiu PWY. AB110. The cost-effectiveness of artificial intelligence-assisted colonoscopy as a primary or secondary screening test in population-based colorectal cancer screening programmes: a Markov modelling study. J Public Health Emerg 2026;10:AB110.

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