ReviewClinical endoscopy2025
Computer-aided diagnosis of colorectal polyps: assisted or autonomous?
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.
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.
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.
Who cites it
4 citing papers in PubMed.
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Diagnostic Accuracy of ChatGPT Model 5.1 in the Optical Characterization of Colorectal Lesions.Cureus · 2026Article
- [Clinical Implementation of Artificial Intelligence in Endoscopy: A Human-Artificial Intelligence Interaction Perspective].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2026Review
- Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes.World journal of gastroenterology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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
Identifiers
What OpenQuestion holds
Registered trials
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.