Khalid Salman, Kim Hojun, Kim Heung Soo
Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Korea; Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor 48109, USA.
Department of Rehabilitation Medicine of Korean Medicine, Dongguk University, 27 Dongguk-ro, Goyang 10326, Korea.
Diabetes Res Clin Pract. 2025 Jun;224:112221. doi: 10.1016/j.diabres.2025.112221. Epub 2025 May 4.
Diabetes mellitus (DM) is a highly prevalent chronic condition with significant health and economic impacts; therefore, an accurate diagnosis is essential for the effective management and prevention of its complications. This review explores the latest advances in artificial intelligence (AI) focusing on machine learning (ML) and deep learning (DL) for the diagnosis of diabetes. Recent developments in AI-driven diagnostic tools were analyzed, with an emphasis on breakthrough methodologies and their real-world clinical applications. This review also discusses the role of various data sources, datasets, and preprocessing techniques in enhancing diagnostic accuracy. Key advancements in integrating AI into clinical workflows and improving early detection are highlighted along with challenges related to model interpretability, ethical considerations, and practical implementation. By offering a comprehensive overview of these advancements and their implications, this review contributes significantly to the understanding of how AI technologies can enhance the diagnosis of diabetes and support their integration into clinical practice, thereby aiming to improve patient outcomes and reduce the burden of diabetes.
糖尿病（DM）是一种高度流行的慢性疾病，对健康和经济有重大影响；因此，准确诊断对于有效管理和预防其并发症至关重要。本综述探讨了人工智能（AI）在糖尿病诊断方面的最新进展，重点关注机器学习（ML）和深度学习（DL）。分析了人工智能驱动的诊断工具的最新发展，重点是突破性方法及其在现实世界中的临床应用。本综述还讨论了各种数据源、数据集和预处理技术在提高诊断准确性方面的作用。强调了将人工智能整合到临床工作流程和改善早期检测方面的关键进展，以及与模型可解释性、伦理考量和实际实施相关的挑战。通过全面概述这些进展及其影响，本综述对理解人工智能技术如何提高糖尿病诊断水平以及支持其融入临床实践做出了重大贡献，从而旨在改善患者预后并减轻糖尿病负担。
