Recently, machine learning (ML) applications have received attention in diabetes and metabolism research. This review briefly provides the basic concepts of ML and specific topics in diabetes research.Exemplary studies are reviewed to provide an overview of the methodology, main findings, limitations, and future research directions for ML-based studies. Well-defined, testable study hypotheses that stem from unmet clinical needs are always the first prerequisite for successful deployment of an MLbased approach to clinical scene. The management of data quality with enough quantity and active collaboration with ML engineers can enhance the ML development process. The interpretable highperformance ML models beyond the black-box nature of some ML principles can be one of the future goals expected by ML and artificial intelligence in the diabetes research and clinical practice settings that is beyond hype. Most importantly, endocrinologists should play a central role as domain experts who have clinical expertise and scientific rigor, for properly generating, refining, analyzing, and interpreting data by successfully integrating ML models into clinical research.

Recently, machine learning (ML) applications have received attention in diabetes and metabolism research. This review briefly provides the basic concepts of ML and specific topics in diabetes research.Exemplary studies are reviewed to provide an overview of the methodology, main findings, limitations, and future research directions for ML-based studies. Well-defined, testable study hypotheses that stem from unmet clinical needs are always the first prerequisite for successful deployment of an MLbased approach to clinical scene. The management of data quality with enough quantity and active collaboration with ML engineers can enhance the ML development process. The interpretable highperformance ML models beyond the black-box nature of some ML principles can be one of the future goals expected by ML and artificial intelligence in the diabetes research and clinical practice settings that is beyond hype. Most importantly, endocrinologists should play a central role as domain experts who have clinical expertise and scientific rigor, for properly generating, refining, analyzing, and interpreting data by successfully integrating ML models into clinical research.

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.