Artificial intelligence in pituitary medicine: what should an endocrinologist know?

Damla N Costa, Rozalina G McCoy, Debraj Mukherjee, Roberto Salvatori

Journal: Pituitary 2026;29(5):

PMID: 42622965

Abstract

Pituitary disorders often include complex radiology imaging, rare clinical presentations, and need for multidisciplinary decision-making and prolonged follow-up, all features that make them a natural target for artificial intelligence (AI). The resulting literature is expanding rapidly, but carries an unfamiliar vocabulary and set of methods, which can leave clinicians without a clear entry point. This short perspective offers such an entry point, succinctly outlining how AI learns from data, how a clinical AI application moves from question to deployment, providing examples, and addressing how to read the field critically.

Address: Department of Medicine, University of Maryland Midtown Campus, Baltimore, USA.; Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, USA.; University of Maryland Institute for Health Computing, North Bethesda, USA.; Department of Neurosurgery and Pituitary Center, Johns Hopkins University School of Medicine, Baltimore, USA.; Division of Endocrinology, Diabetes and Metabolism and Pituitary Center, Johns Hopkins University School of Medicine, 1830 East Monument Street #333, Baltimore, MD, 21287, USA. [email protected].

DOI: 10.1007/s11102-026-01747-w

MeSH Terms: Humans, Artificial Intelligence, Pituitary Diseases, Endocrinologists, Pituitary Gland, Endocrinology, Machine Learning