Evidence map›Paper›PMID 40400307›Full record

ReviewClinical endoscopy2025

Computer-aided diagnosis of colorectal polyps: assisted or autonomous?

Yuichi Mori, Cesare Hassan

Abstract readReview
In one paragraph

Review in Clinical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
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

2 authors.

Yuichi MoriClinical Effectiveness Research Group, Faculty of Medicine, University of Oslo, Oslo, Norway.
Cesare HassanDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.

Funding

Associazione Italiana per la Ricerca sul Cancro Bando PNRR-MCNT2-2023-12377041Associazione Italiana per la Ricerca sul Cancro IG 2022-ID. 27843 projectAssociazione Italiana per la Ricerca sul Cancro IG 2023-ID. 29220 projectEuropean Commission 101057099Japan Society for the Promotion of Science 22H03357
6 · The paper itself

Abstract

Computer-aided diagnosis (CADx) in colonoscopy aims to improve the accuracy of diagnosing small polyps; however, its integration into clinical practice remains challenging. Human-artificial intelligence (AI) collaboration, which is expected to enhance optical diagnosis, has shown limited success in clinical trials, with studies indicating no significant improvement in human-only performance. Conversely, autonomous CADx systems that operate independently of clinicians have demonstrated superior diagnostic accuracy in some studies, suggesting their potential for efficiency, consistency, and standardization in healthcare. However, the adoption of autonomous AI raises ethical, legal, and practical concerns such as accountability for errors, loss of clinical context, and clinician or patient distrust. The decision between using CADx as an assistant or as an autonomous system may depend on the clinical scenario. Autonomous systems can standardize routine screening for low-risk patients, whereas assistive systems may complement expertise in complex cases. Regardless of the model used, robust regulatory frameworks and clinician training are essential to ensure safety and maintain trust. Balancing the strengths of AI with the critical role of human judgment is the key to optimizing outcomes and navigating the complex implications of integrating CADx technologies into colonoscopy practice.

Indexed as

AdenomaArtificial intelligenceColonoscopy

Identifiers

PMID40400307
PMCPMC12314623

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.