Endocrinol Metab.  2020 Mar;35(1):71-84. 10.3803/EnM.2020.35.1.71.

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

  • 1Division of Endocrinology and Metabolism, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea. nkhong84@yuhs.ac


Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.


Machine learning; Artificial intelligence; Deep learning; Endocrinology; Metabolism; Diabetes; Osteoporosis; Pituitary; Adrenal; Thyroid

MeSH Terms

Artificial Intelligence
Cooperative Behavior
Data Accuracy
Machine Learning*
Thyroid Gland
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