Endocrine · 2025
Machine learning in endocrinology: current applications and future perspectives.
СтатьяСистематический обзорНазвание и аннотация: Перевод в очереди
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Kamińska Magdalena, Trofimiuk-Müldner Małgorzata, Sokołowski Grzegorz, Hubalewska-Dydejczyk Alicja
Аннотация
PURPOSE: In recent years, endocrinology research has increasingly focused on machine learning (ML) applications. ML offers the possibility of utilizing large data sets and extracting imperceptible patterns. It might contribute in optimizing healthcare outcomes and unveiling new understandings of the intricate mechanisms of endocrine disorders. This review covers the basic aspects of ML and highlights specific areas of endocrinology with potential of ML application.
METHODS: This narrative review with a systematic literature search comprises studies on endocrine conditions with ML methods used in statistical analysis, published between January 2000 and December 2024.
RESULTS: A total of 1130 studies were analyzed. Thyroid-related research was the most prevalent, followed by studies concerning the pituitary, adrenal and parathyroid glands. ML applications included medical imaging analysis, tumor classification, treatment response prediction, complication risk estimation and identification of molecular markers.
CONCLUSION: ML has the potential to enhance the diagnosis, treatment and understanding of endocrine diseases. However, the use of ML is still limited by issues such as lack of model transparency, data imbalance and difficulties with clinical implementation. To enable safe and effective application of ML in endocrinology, further validation, interdisciplinary collaboration and standardized approaches are essential.
METHODS: This narrative review with a systematic literature search comprises studies on endocrine conditions with ML methods used in statistical analysis, published between January 2000 and December 2024.
RESULTS: A total of 1130 studies were analyzed. Thyroid-related research was the most prevalent, followed by studies concerning the pituitary, adrenal and parathyroid glands. ML applications included medical imaging analysis, tumor classification, treatment response prediction, complication risk estimation and identification of molecular markers.
CONCLUSION: ML has the potential to enhance the diagnosis, treatment and understanding of endocrine diseases. However, the use of ML is still limited by issues such as lack of model transparency, data imbalance and difficulties with clinical implementation. To enable safe and effective application of ML in endocrinology, further validation, interdisciplinary collaboration and standardized approaches are essential.