Artificial Intelligence in Pituitary Disease

Artificial intelligence has shown great promise in clinical research for pituitary diseases, aiding in differential diagnosis, tumor subtype prediction, invasion assessment, surgical outcomes, complication forecasting, and providing intraoperative decision support. For functional pituitary adenomas, models can predict medication response. However, translating these advancements into real-world clinical care requires addressing key challenges: ensuring generalizability, enhancing clinical applicability, demonstrating tangible improvements in patient prognosis, and leveraging foundation models to create reliable, adaptable solutions.