Machine Learning Expands Across Endocrinology
Machine learning "might provide remarkable benefits to the endocrine field."
Recent advancements in machine learning (ML) applications within endocrinology have been detailed in a review led by Dr. Alicja Hubalewska-Dydejczyk. Over 1,130 studies from January 2000 to December 2024 were analyzed, focusing mainly on thyroid diseases, which constituted 68% of the research. Applications included imaging enhancements, risk predictions, and treatment-response models. Although ML demonstrated significant potential in diagnosing and managing endocrine disorders, notable limitations such as lack of model transparency and data imbalance persist. Continued interdisciplinary collaboration is essential for advancing ML integration in clinical practice.
1. ML applications in endocrinology have grown rapidly since 2000. 2. 1,130 studies reviewed, predominantly focused on thyroid disorders. 3. ML techniques include imaging, risk prediction, and modeling treatment response. 4. Key limitations included model transparency and data imbalance. 5. High diagnostic performance achieved in certain ML applications like thyroid ultrasound. 6. Collaborative efforts are essential for effective ML integration in clinical settings.