Centre for Outcome Research & Evaluation, McGill University Health Centre, Montréal, Québec, Canada; Division of Experimental Medicine, Faculty of Medicine and Health Science, McGill University, Montréal, Québec, Canada.
Division of Experimental Medicine, Faculty of Medicine and Health Science, McGill University, Montréal, Québec, Canada; DREAM-CV Lab, Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.
Can J Cardiol. 2024 Oct;40(10):1922-1933. doi: 10.1016/j.cjca.2024.07.028. Epub 2024 Aug 5.
Type 2 diabetes mellitus (T2DM), a complex metabolic disorder that burdens the health care system, requires early detection and treatment. Recent strides in digital health technologies, coupled with artificial intelligence (AI), may have the potential to revolutionize T2DM screening, diagnosis of complications, and management through the development of digital biomarkers. This review provides an overview of the potential applications of AI-driven biomarkers in the context of screening, diagnosing complications, and managing patients with T2DM. The benefits of using multisensor devices to develop digital biomarkers are discussed. The summary of these findings and patterns between model architecture and sensor type are presented. In addition, we highlight the pivotal role of AI techniques in clinical intervention and implementation, encompassing clinical decision support systems, telemedicine interventions, and population health initiatives. Challenges such as data privacy, algorithm interpretability, and regulatory considerations are also highlighted, alongside future research directions to explore the use of AI-driven digital biomarkers in T2DM screening and management.
2 型糖尿病（T2DM）是一种复杂的代谢紊乱疾病，给医疗保健系统带来了沉重负担，需要早期发现和治疗。最近，数字健康技术的进步，加上人工智能（AI），可能通过开发数字生物标志物来彻底改变 T2DM 的筛查、并发症诊断和管理。本综述概述了 AI 驱动的生物标志物在 T2DM 筛查、诊断并发症和管理患者方面的潜在应用。讨论了使用多传感器设备开发数字生物标志物的好处。总结了这些发现和模型架构与传感器类型之间的模式。此外，我们强调了 AI 技术在临床干预和实施中的关键作用，包括临床决策支持系统、远程医疗干预和人群健康计划。还强调了数据隐私、算法可解释性和监管考虑等挑战，以及探索在 T2DM 筛查和管理中使用 AI 驱动的数字生物标志物的未来研究方向。
