Researchers from the Mayo Clinic presented a novel artificial intelligence screening model at ENDO 2026 in Chicago that can identify patients at high risk for primary aldosteronism. Led by Dr. Frank Lee, the study leverages three decades of routine electronic health record data to address the widespread underdiagnosis of this condition, a leading secondary cause of hypertension that significantly elevates cardiovascular and renal risks. The AI framework was developed using de-identified clinical data spanning 1986 to 2025 from the Mayo Clinic Platform, a federated infrastructure housing multimodal records for over 22,000 individuals. Utilizing an XGBoost machine learning architecture, the model processes routine clinical variables including patient demographics, systolic blood pressure readings, serum potassium levels, relevant diagnostic codes for hypertension and hypokalemia, and prescribed antihypertensive or potassium-supplement medications. Following initial development, the system was validated against a larger cohort of 225,887 adults diagnosed with hypertension. Validation testing demonstrated that the model successfully predicts primary aldosteronism risk up to twelve months prior to conventional clinical diagnosis. When calibrated to a low-risk exclusion threshold, the algorithm correctly identified more than ninety percent of confirmed cases while generating a false-negative rate below ten percent. In trials involving hypertensive patients who had never undergone prior screening, the model successfully flagged approximately two-thirds of the population as candidates for targeted diagnostic workups. Primary aldosteronism affects an estimated twenty percent of hypertensive individuals yet remains frequently overlooked due to nonspecific symptoms and limited clinician screening protocols. The condition substantially increases the likelihood of stroke, atrial fibrillation, heart failure, and chronic kidney disease. Effective treatments exist that can mitigate these complications and reduce long-term healthcare expenditures. The 2025 clinical practice guidelines from the Endocrine Society explicitly recommend expanded screening protocols. By operating entirely on routinely captured medical records, the AI tool offers a scalable, cost-effective solution to bridge current diagnostic gaps and standardize early intervention in primary care settings.