Artificial Intelligence for Aging and Age-Related Healthcare: Diagnosis, Prediction, and Personalized Care

Rapid global population aging is reshaping healthcare systems, with older adults accounting for an increasing share of disease burden, healthcare utilization, and costs. Age-related conditions—such as frailty, multimorbidity, cognitive decline, cardiovascular and metabolic diseases, and neurodegeneration—are often interrelated, heterogeneous, and dynamically evolving over time. Concurrently, geriatric care is generating increasingly rich and complex data from electronic health records, medical imaging, laboratory testing, genomics, wearables, ambient sensors, and social, functional, and behavioral assessments.Artificial intelligence (AI) and machine learning (ML) offer powerful approaches for transforming these high-dimensional data into clinically actionable insights for prevention, early diagnosis, prognosis, and personalized care. However, many AI solutions have been developed and validated in younger or highly selected populations and therefore may not generalize well to older adults, whose health profiles are shaped by multimorbidity, polypharmacy, functional impairment, and social vulnerability. Substantial challenges remain regarding data quality, bias, interpretability, and real-world implementation in geriatric settings. These gaps underscore the urgent need for rigorous, clinically grounded research on AI for aging and age-related healthcare, spanning the continuum from healthy aging to advanced frailty and end-of-life care.This Research Topic aims to bring together an interdisciplinary collection of studies on the design, validation, and implementation of AI methods specifically tailored to aging populations and age-related healthcare. We seek contributions that move beyond proof-of-concept algorithms to address clinical utility, patient-centered outcomes, and integration into real-world geriatric practice. The Topic will highlight how AI and ML can improve risk stratification, early detection of geriatric syndromes, longitudinal monitoring of health trajectories, and personalized care planning for older adults across diverse care settings. We are particularly interested in work that addresses fairness, transparency, and the ethical use of AI in vulnerable older populations, and that bridges technical innovation with clinical, public health, and health services research perspectives. By connecting experts across disciplines, this collection aims to advance safe, equitable, and impactful AI to support healthy and dignified aging.We welcome submissions that report original empirical findings, novel methods, or critical syntheses related to AI in aging and age-related healthcare. Acceptable article types include, but are not limited to, Original Research, Systematic or Scoping Reviews, Brief Research Reports, Methods articles, Case Reports, Perspectives, and Policy and Practice Reviews in line with Frontiers in Medicine guidelines.Topics of interest include, but are not limited to:• AI/ML for early diagnosis, risk prediction, and prognosis of age-related diseases and geriatric syndromes (e.g., frailty, falls, delirium, cognitive decline).• Multimodal and longitudinal data integration (e.g., EHR, imaging, sensors, digital biomarkers) to monitor older adults in clinical and community settings.• AI-driven clinical decision support for personalized geriatric care, polypharmacy management, rehabilitation, and palliative care.• Explainability, bias, fairness, ethics, and regulatory considerations for AI applications involving older adults.• Implementation studies, real-world evidence, clinical trials, and health economic or organizational evaluations of AI-enabled geriatric care pathways. Topic editors: Heng Li, Xueping Peng, Yi Ding, YIJIE PAN, YAN ZHANG Source: https://www.frontiersin.org/research-topics/76509/artificial-intelligence-for-aging-and-age-related-healthcare-diagnosis-prediction-and-personalized-care