ArticleBMJ open gastroenterology2025
Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool.
Article in BMJ open gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveArtificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is an AI algorithm that can effectively classify colonic biopsies into normal versus abnormal categories, designed to automatically report normal cases. We performed a retrospective pathological and clinical review of the errors made by IGUANA.
methodsFalse negative (FN) errors were the primary focus due to the greatest propensity for harm. Pathological evaluation involved assessment of whole slide image (WSI) quality, precise diagnoses for each missed entity and identification of factors impeding diagnosis. Clinical evaluation scored the impact of each error on the patient and detailed the type of impact in terms of missed diagnosis, investigations or treatment.
resultsAcross 5054 WSIs from 2080 UK National Health Service patients there were 220 FN errors across 164 cases (4.4% of WSI, 7.9% of cases). Diagnostic errors varied from cases of adenocarcinoma to mild inflammation. 88.4% of FN errors would have no impact on patient care, with only one error causing major patient harm. Factors that protected against harm included biopsies being low-risk polyps or diagnostic features were detected in other biopsies.
conclusionMost FN errors would not result in patient harm, suggesting that even with a 7.9% case-level error rate, this AI tool might be more suitable for adoption than statistics portray. Consideration of the clinical context of AI tool errors is essential to facilitate safe implementation.
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.