The Role of Artificial Intelligence in Equine Colic: A Scoping Review of Diagnostic, Prognostic, and Decision-Support Applications.

AI models, particularly random forest, show strong prognostic potential for equine colic but require external validation before clinical adoption.

This scoping review systematically maps the current landscape of artificial intelligence (AI) applications in equine colic diagnosis, prognosis, and clinical decision-support. Sixteen studies published between 2015 and 2025 were included, encompassing 104 distinct AI models. The majority of models targeted prognostic prediction, particularly survival outcome assessment. Logistic regression was the most frequently employed algorithm, followed by random forest (RF) and ensemble methods. RF models demonstrated consistently strong discriminative performance for survival prediction, with AUC values ranging from 0.79 to 0.99 and accuracy frequently exceeding 80%. Despite these promising results, the review identifies substantial methodological limitations that impede clinical translation. External validation was rarely performed, calibration assessment was largely absent, and preprocessing strategies were inconsistently reported across studies. Class imbalance—a common challenge in clinical veterinary datasets—was infrequently addressed, further compromising model generalizability and reproducibility. The authors emphasize that while AI tools show meaningful potential to augment clinical decision-making in equine colic management, significant barriers remain before routine clinical implementation is feasible. Recommendations for future research include multicenter data collaboration, rigorous external validation protocols, calibration reporting, adoption of standardized reporting frameworks (such as TRIPOD), integration of explainable AI methodologies, and development of multimodal monitoring systems. This review provides a valuable foundational map for researchers and clinicians seeking to advance evidence-based AI applications in equine emergency medicine.

This summary was distilled by AI and may occasionally misinterpret data. Confirm critical details with the primary literature before clinical application.