Anwar Aamir, Rana Simran, Pathak Priya
Department of Pharmacy, Amity University, Lucknow campus, 226010, Lucknow, Uttar Pradesh, India.
J Endocrinol Invest. 2025 Feb 19. doi: 10.1007/s40618-025-02548-x.
This review explores the significant role of artificial intelligence (AI) in managing metabolic disorders like diabetes, obesity, metabolic dysfunction-associated fatty liver disease (MAFLD), and thyroid dysfunction. AI applications in this context encompass early diagnosis, personalized treatment plans, risk assessment, prevention, and biomarker discovery for early and accurate disease management. This review also delves into techniques involving machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, and reinforcement learning associated with AI and their application in metabolic disorders. The following study also enlightens the challenges and ethical considerations associated with AI implementation, such as data privacy, model interpretability, and bias mitigation. We have reviewed various AI-based tools utilized for the diagnosis and management of metabolic disorders, such as Idx, Guardian Connect system, and DreaMed for diabetes. Further, the paper emphasizes the potential of AI to revolutionize the management of metabolic disorders through collaborations among clinicians and AI experts, the integration of AI into clinical practice, and the necessity for long-term validation studies. The references provided in the paper cover a range of studies related to AI, ML, personalized medicine, metabolic disorders, and diagnostic tools in healthcare, including research on disease diagnostics, personalized therapy, chronic disease management, and the application of AI in diabetes care and nutrition.
本综述探讨了人工智能（AI）在管理糖尿病、肥胖症、代谢功能障碍相关脂肪性肝病（MAFLD）和甲状腺功能障碍等代谢紊乱方面的重要作用。在这种情况下，人工智能的应用包括早期诊断、个性化治疗方案、风险评估、预防以及用于早期和准确疾病管理的生物标志物发现。本综述还深入研究了涉及机器学习（ML）、深度学习（DL）、自然语言处理（NLP）、计算机视觉以及与人工智能相关的强化学习的技术及其在代谢紊乱中的应用。以下研究还阐明了与人工智能实施相关的挑战和伦理考量，例如数据隐私、模型可解释性和偏差缓解。我们回顾了用于代谢紊乱诊断和管理的各种基于人工智能的工具，如用于糖尿病的Idx、Guardian Connect系统和DreaMed。此外，本文强调了人工智能通过临床医生与人工智能专家之间的合作、将人工智能整合到临床实践中以及进行长期验证研究的必要性，从而彻底改变代谢紊乱管理的潜力。本文提供的参考文献涵盖了一系列与人工智能、机器学习、个性化医疗、代谢紊乱以及医疗保健中的诊断工具相关的研究，包括疾病诊断、个性化治疗、慢性病管理以及人工智能在糖尿病护理和营养方面的应用研究。
