ArticleClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association2026
Development and External Validation of a Large Language Model-Based Clinical Decision-Support System for Colonoscopy Surveillance Intervals.
Article in Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
BACKGROUND &
aimsAdherence to guideline-based colonoscopy surveillance intervals remains suboptimal. Large language models show promise for automating interval assignment, but prior studies relied on proprietary models and have variably assessed generalizability. We developed and externally validated a clinical decision-support system that integrates locally deployable, open-source large language model-based variable extraction with a rules engine that encodes the 2020 United States Multi-Society Task Force guidelines.
methodsWe assembled development (n = 256), internal (n = 450), and external validation (n = 415) cohorts of colonoscopy-pathology dyads. We developed a 3-stage clinical decision-support system including: (1) large language model-based extraction of diagnosis-bearing sections from pathology reports; (2) large language model-based extraction of surveillance-relevant variables; and (3) deterministic assignment of surveillance intervals using United States Multi-Society Task Force logic. Seven open-source large language models (3.8-32B parameters) were benchmarked; the highest-performing model by macro-averaged F1 score was locked prior to validation. The primary outcome was guideline-concordant interval assignment (target, 90%); secondary outcomes included extraction performance and hallucination rate.
resultsThe best-performing open-source model, Gemma-27B-Instruct (macro-averaged F1 score, 0.992), was locked. The clinical decision-support system achieved 94.0% guideline-concordant surveillance interval assignment in internal validation (95% confidence interval, 91.8%-96.0%; κ = 0.92; 95% confidence interval, 0.89-0.95) and 92.8% concordance in external validation (95% confidence interval, 90.1%-95.2%; κ = 0.88; 95% confidence interval, 0.84-0.91). Misclassifications were primarily due to adenoma miscounting and diagnostic biopsies misinterpreted as resections. Hallucination-related errors occurred in fewer than 1% of cases.
conclusionsWe report an externally validated, hybrid large language model rules-based clinical decision-support system for colonoscopy surveillance that achieves >90% concordance while preserving transparency, auditability, and guideline traceability.
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.