From explainability to clinical actionability: translating artificial intelligence models into decision support for endocrine disease management.

Artificial intelligence (AI) models are increasingly reported in endocrine disease management, but clinical value cannot be inferred from discrimination, model complexity, or explainability alone. This narrative review synthesizes evidence from diabetes and diabetic kidney disease (DKD), thyroid disease, polycystic ovary syndrome (PCOS; with attention to the proposed polyendocrine metabolic ovarian syndrome, PMOS, terminology transition), and obesity/GLP-1-based metabolic management through an actionability framework. We define clinical actionability as the link between a meaningful endocrine prediction and transparent reporting, bias and applicability appraisal, external or temporal validation, calibration, threshold-based utility, interpretable output, workflow integration, fairness assessment, and post-deployment monitoring. Diabetes/DKD provides a comparatively broad evidence base, including decision-support examples, non-invasive triage, calibration, decision-curve analysis, and early deployment-oriented work. Thyroid AI increasingly reports validation and clinical-utility metrics for nodule triage, biopsy decisions, and risk prediction, but prospective workflow evidence remains limited. PCOS/PMOS AI addresses diagnostic and reproductive-endocrine gaps, yet requires broader phenotype alignment, fairness analysis, and multi-setting validation during the PMOS terminology transition. Obesity/GLP-1 evidence now includes response-prediction, benefit-stratification, treatment-intensification, persistence, and remote-care examples, but it does not establish AI-guided treatment selection, adherence/tolerability prediction, or monitoring. Across modules, differences lie less in headline discrimination metrics, including the area under the receiver operating characteristic curve (AUC), than in validation, utility, workflow, fairness, and monitoring evidence. Endocrine AI should move from explainable prediction toward decision support that can be interpreted, thresholded, acted on, monitored, and revised in clinical workflows.