From the Journals

Machine Learning Expands Across Endocrinology

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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.

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