Artificial Intelligence in Gastroenterology: Towards Explainable, Generalisable and Clinically Actionable Decision Support.
- 1Department of Audiovisual Techniques and Media Production, Vocational School of Technical Sciences, Firat University, Elazig 23119, Türkiye.
- 2Vocational School of Technical Sciences, Firat University, Elazig 23119, Türkiye.
- 3Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, Türkiye.
Abstract
Artificial intelligence is increasingly used in gastroenterology for endoscopic detection and characterisation, disease-activity assessment, risk prediction, treatment-response estimation and workflow support. However, high retrospective accuracy does not necessarily translate into reliable or beneficial clinical decision support. This structured narrative review examines the clinical development of artificial intelligence across gastrointestinal endoscopy, inflammatory bowel disease, hepatology and pancreaticobiliary diseases through three translational dimensions: explainability, generalisability and clinical actionability. Communication, human oversight and lifecycle governance are considered cross-cutting determinants of implementation. The evidence is most mature in gastrointestinal endoscopy, particularly for computer-aided polyp detection, although benefits vary across operators, indications and practice settings. Applications in inflammatory bowel disease, hepatology and pancreaticobiliary care remain predominantly retrospective, with limited prospective evidence that model outputs alter management or improve patient outcomes. Explainability is commonly represented by heatmaps or feature-attribution methods, but rarely evaluated in terms of clinician understanding, calibrated trust or patient communication. External validation is becoming more frequent, yet geographic and technological transportability, calibration and performance under dataset shift remain insufficiently assessed. Prospective implementation and health-economic studies are uncommon, and available economic evidence is largely model-based. Clinical artificial intelligence should therefore be evaluated as a sociotechnical intervention comprising the model, interface, users, workflow, communication process and associated care pathway. Translation requires clinically defined use cases, representative multicentre validation, human-centred evaluation, prospective assessment of benefits and harms, and continuous post-deployment surveillance. Clinical AI in gastroenterology should complement clinical expertise through transparent, contestable and continuously monitored decision support that preserves clinician accountability and supports informed, patient-centred decisions.
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BibTeX
@article{baydili2026artificial,
title = {{Artificial Intelligence in Gastroenterology: Towards Explainable, Generalisable and Clinically Actionable Decision Support.}},
author = {İsmail Baydili and Burak Tasci and Sengul Dogan and Turker Tuncer},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = sep,
doi = {10.3390/diagnostics16193182},
url = {https://europepmc.org/article/MED/42882337},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Gastroenterology: Towards Explainable, Generalisable and Clinically Actionable Decision Support.
AU - İsmail Baydili
AU - Burak Tasci
AU - Sengul Dogan
AU - Turker Tuncer
PY - 2026
DA - 2026/09/30
JO - Diagnostics (Basel, Switzerland)
DO - 10.3390/diagnostics16193182
UR - https://europepmc.org/article/MED/42882337
AB - Artificial intelligence is increasingly used in gastroenterology for endoscopic detection and characterisation, disease-activity assessment, risk prediction, treatment-response estimation and workflow support. However, high retrospective accuracy does not necessarily translate into reliable or beneficial clinical decision support. This structured narrative review examines the clinical development of artificial intelligence across gastrointestinal endoscopy, inflammatory bowel disease, hepatology and pancreaticobiliary diseases through three translational dimensions: explainability, generalisability and clinical actionability. Communication, human oversight and lifecycle governance are considered cross-cutting determinants of implementation. The evidence is most mature in gastrointestinal endoscopy, particularly for computer-aided polyp detection, although benefits vary across operators, indications and practice settings. Applications in inflammatory bowel disease, hepatology and pancreaticobiliary care remain predominantly retrospective, with limited prospective evidence that model outputs alter management or improve patient outcomes. Explainability is commonly represented by heatmaps or feature-attribution methods, but rarely evaluated in terms of clinician understanding, calibrated trust or patient communication. External validation is becoming more frequent, yet geographic and technological transportability, calibration and performance under dataset shift remain insufficiently assessed. Prospective implementation and health-economic studies are uncommon, and available economic evidence is largely model-based. Clinical artificial intelligence should therefore be evaluated as a sociotechnical intervention comprising the model, interface, users, workflow, communication process and associated care pathway. Translation requires clinically defined use cases, representative multicentre validation, human-centred evaluation, prospective assessment of benefits and harms, and continuous post-deployment surveillance. Clinical AI in gastroenterology should complement clinical expertise through transparent, contestable and continuously monitored decision support that preserves clinician accountability and supports informed, patient-centred decisions.
SN - 2075-4418
ER -