Evidence map›Paper›PMID 42682389›Full record

ArticleInternational journal of surgery (London, England)2026

Computer-Assisted Detection (CADe) of colonic polyps during colonoscopy reduces cancer-related healthcare costs - a single Singaporean institution cost effectiveness analysis.

Gabrielle E Koh, Jayden W Yap, Tan J Han, Fung-Joon Foo, Juinn Huar Kam, Baldwin Yeung Po Man, James W Li, Clarence Kwan, Frederick H Koh

Abstract read
In one paragraph

Article in International journal of surgery (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Gabrielle E KohDepartment of General Surgery, Sengkang General Hospital, Singapore.ORCID https://orcid.org/0009-0004-6190-9039
Jayden W YapYong Loo Lin School of Medicine, National University of Singapore, Singapore.
Tan J HanYong Loo Lin School of Medicine, National University of Singapore, Singapore.
Fung-Joon FooColorectal service, Department of Surgery, Sengkang General Hospital, Singapore.ORCID https://orcid.org/0000-0002-7347-4894
Juinn Huar KamDepartment of General Surgery, Sengkang General Hospital, Singapore.ORCID https://orcid.org/0000-0002-2478-9689
Baldwin Yeung Po ManDepartment of General Surgery, Sengkang General Hospital, Singapore.ORCID https://orcid.org/0000-0002-0656-2181
James W LiDepartment of Gastroenterology, Sengkang General Hospital, Singapore.ORCID https://orcid.org/0000-0002-5241-4278
Clarence KwanDepartment of General Surgery, Sengkang General Hospital, Singapore.ORCID https://orcid.org/0000-0002-8221-6803
Frederick H KohColorectal service, Department of Surgery, Sengkang General Hospital, Singapore.ORCID https://orcid.org/0000-0002-3717-4480

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: Colonoscopy remains as an invaluable diagnostic and therapeutic tool used for the screening and evaluation of colonic pathologies. Periodic endoscopic screening allowing for early detection and removal of pre-malignant lesions, resulting in significant overall decreased incidence and mortality from colorectal cancer. Despite its added costs, artificial intelligence (AI) has been increasingly utilized in the field of endoscopy, boasting benefits of improving adenoma detection rates while reducing endoscopists' mental fatigue intra-procedurally. As such, further evaluation of the financial impact of such AI-usage in screening colonoscopies was performed to assess if routine use of AI-guidance in screening colonoscopies was justifiable. Methods: This study conducted a cost effectiveness analysis of the GI Genius™ CADx Intelligent Endoscopy Module (US-DG-2000309, 2021 Medtronic) system in our local Singaporean tertiary public healthcare institution. A decision tree model was used to calculate Incremental Cost Effectiveness Ratio (ICER), comparing cancer-related costs for patients who underwent conventional colonoscopy versus colonoscopy with AI guidance. Results: With a calculated ICER value of 0.72, our study suggests that AI-assisted colonoscopy was indeed cost effective. Quality-Adjusted Life Years (QALY) values derived for each subgroup of patients further demonstrated overall improved quality of life reported by patients who underwent AI-assisted colonoscopy, as compared to conventional colonoscopy. Conclusion: Our study supports the incorporation of AI guidance into routine colonoscopic evaluation in view of increased cost effectiveness and improved overall quality of life achieved. However, further studies and analysis must be undertaken before reliably determining the cost-effectiveness of AI on regional or international scales.

Indexed as

adenoma detectionartificial intelligencecolonoscopycolorectal cancercost effectivenesspolyp detection

Identifiers

PMID42682389
PMCPMC13249318

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.