Clin-STAR Corner: Practice Changing Advances at the Interface of Artificial Intelligence/Machine Learning and Geriatrics

Abstract

Artificial intelligence (AI) methods, including machine learning (ML), are transforming healthcare by enabling personalized interventions that integrate multimodal data to support rehabilitation, preventive care, and remote monitoring. Despite their broad potential, older adults remain underrepresented in model development, raising concerns about bias and limited generalizability. As AI/ML adoption expands, it is essential to critically appraise emerging tools to ensure ethical, equitable, person-centered implementation in aging populations and long-term randomized evaluations. A structured MEDLINE search identified randomized controlled trials (RCTs) published between January 2023 and December 2025 that evaluated AI/ML-based interventions in adults ≥ 65 years. Of 31 records identified, 19 underwent abstract screening, seven underwent full-text assessment, and four articles were identified as meeting all criteria for inclusion, which focused on scalability, clinical impact, and methodological rigor. Across postoperative rehabilitation and preventive care, AI/ML interventions demonstrated meaningful clinical benefits. Transformative applications of AI/ML described robotic systems quantifying weight-bearing, algorithm-guided paired rehabilitation interventions, paired exercises in geriatric hip fracture rehabilitation, smartphone platforms delivering continuously personalized exercise programs, and conversational chatbots providing tailored vaccine counseling. The included RCTs show that AI-driven interventions can enhance physical recovery, support psychosocial well-being, and improve uptake of preventive measures compared with conventional approaches. Key considerations for future implementation include digital health literacy, long-term follow-up, and potential bias arising from healthier or more motivated study populations. As evidence grows, geriatric innovation must prioritize safety, ethics, and equitable access to ensure that technological precision enhances, rather than replaces, person-centered care.