Yaru Wang, Miao Miao, Qingqing Wang, Yuyin Yin, Haijuan Zhao, Shuang Zhao, Han Yang, Xin Wang
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引用次数: 0
Abstract
Artificial intelligence (AI) is revolutionizing clinical trials in geriatric populations by addressing the unique challenges of aging-related diseases and patient heterogeneity. Advanced AI techniques, including machine learning, deep learning, and AI-driven digital health platforms, enable intelligent patient stratification, risk prediction, and real-time monitoring tailored to older adults. AI facilitates the design and execution of decentralized clinical trials, improving accessibility and compliance among older participants. Moreover, AI-powered digital twins and predictive models enhance safety assessments and treatment personalization, optimizing therapeutic outcomes. By integrating multi-omics data, electronic health records, and wearable device outputs, AI enables precise and dynamic decision-making throughout the trial lifecycle. This approach not only increases trial efficiency and accuracy but also supports ethical, patient-centered research practices. This review explores the transformative role of AI in geriatric clinical trials, outlining key advancements, practical challenges, and strategic directions for establishing AI as a catalyst for precision medicine in aging populations.
期刊介绍:
Drugs & Aging delivers essential information on the most important aspects of drug therapy to professionals involved in the care of the elderly.
The journal addresses in a timely way the major issues relating to drug therapy in older adults including: the management of specific diseases, particularly those associated with aging, age-related physiological changes impacting drug therapy, drug utilization and prescribing in the elderly, polypharmacy and drug interactions.