Best Clinical AI Tools for Endocrinology

Diabetes management dominates endocrinology practice, and the pace of change in diabetes therapeutics has been extraordinary. The 2008 EMPA-REG OUTCOME trial, which demonstrated cardiovascular mortality reduction with empagliflozin, fundamentally altered the treatment algorithm — moving SGLT2 inhibitors from a third-line afterthought to a first-line consideration in patients with cardiovascular disease. Subsequent landmark trials (LEADER, SUSTAIN-6, DECLARE, CREDENCE, DAPA-CKD) have similarly elevated GLP-1 RAs and SGLT2 inhibitors for their cardiorenal benefits. CDS tools must track these changes and incorporate them into actionable treatment guidance. Beyond diabetes, endocrinology CDS needs span thyroid disease (the algorithmic evaluation of thyroid nodules via ATA Bethesda criteria, management of Graves' disease and hypothyroidism), adrenal disorders (Cushing's workup, adrenal incidentaloma management), calcium and bone metabolism (osteoporosis treatment sequencing, primary hyperparathyroidism surgical criteria), and pituitary disease. Each of these involves evidence-based algorithms that benefit from CDS tool integration. Insulin management deserves special mention. Basal-bolus insulin dosing, correction factor calculations, insulin pump settings, and the growing use of hybrid closed-loop systems all involve mathematical precision that CDS tools can support. The transition from multiple daily injections to insulin pump therapy, and the integration of continuous glucose monitoring data, represent workflow decisions where evidence-based guidance adds significant value.