Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

Area of Science:

• Endocrinology and Metabolism Research
• Artificial Intelligence in Healthcare

Background:

• Machine learning (ML) has gained significant traction in endocrinology over the past decade.
• Existing research often focuses on ML deployment without a clear roadmap for clinical integration.

Purpose of the Study:

• To review fundamental ML concepts and their applications in endocrinology and metabolism.
• To identify key factors for successful ML implementation in clinical practice.
• To stimulate future research directions for ML in endocrinology.

Main Methods:

• Systematic review of relevant studies employing ML in endocrinology and metabolism.
• Discussion of ML methodologies, findings, and limitations.
• Analysis of prerequisites for advancing ML in clinical endocrinology.

Main Results:

• ML has been actively deployed across various endocrinology and metabolism research topics.
• Key findings, methodologies, and limitations of current ML applications are summarized.
• Data quality, interdisciplinary collaboration, and interpretable models are crucial for progress.

Conclusions:

• Successful integration of ML in endocrinology requires a focus on clinical needs and data quality.
• Collaboration between clinicians and ML engineers, alongside interpretable models, is essential.
• Endocrinologists remain central to guiding ML development and application in clinical practice.