Towards transparent and reliable AI in gastroenterology: pragmatic recommendations for evaluation and reporting

Artificial intelligence (AI) is expanding in gastroenterology, particularly in endoscopy and imaging, where models support detection, classification, and risk stratification. However, translation into routine practice is hindered by inconsistent evaluation practices and limited clinical interpretability of reported metrics. We do not propose a generic reporting checklist; rather, we address a specific operational gap: insufficient support for clinicians in translating model outputs into use-case-specific decision thresholds, calibration assessment, and decision-analytic evaluation of clinical utility. These recommendations are based on narrative synthesis and expert opinion. We argue that evaluation should be anchored to pre-specified, clinically justified decision thresholds - or to a transparent interval of plausible thresholds elicited for the intended use case and prospective user community - rather than relying on global measures such as AUROC. We highlight how prevalence, calibration, and asymmetric error costs shape sensitivity, specificity, and predictive values. We recommend visualizations -ROC curves with marked operating points, calibration plots, and decision curve analysis - to communicate threshold-dependent behavior, probability reliability, and expected clinical utility. Beyond summary metrics, we emphasize the need for external validation, structured error analysis, and robustness assessment under distribution shift. Finally, we discuss transparency requirements under emerging European regulatory frameworks. These recommendations aim to support safer and more interpretable adoption of AI tools in gastroenterology.