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Related Experiment Videos

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

Tue T Te1,2, Alex A T Bui3, William Speier3

  • 1Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles (UCLA), Los Angeles, California, USA.

Journal of the American Geriatrics Society
|May 23, 2026
PubMed
Summary

Artificial intelligence (AI) and machine learning (ML) show promise in improving care for older adults. However, ensuring equitable access and addressing potential bias in AI/ML interventions is crucial for effective implementation in geriatric populations.

Keywords:
artificial intelligencemachine learningolder adultsrandomized controlled trial

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Area of Science:

  • Geriatric Medicine
  • Artificial Intelligence in Healthcare
  • Digital Health

Background:

  • Older adults are underrepresented in artificial intelligence (AI) and machine learning (ML) model development, leading to concerns about bias and generalizability.
  • The ethical, equitable, and person-centered implementation of AI/ML in aging populations requires critical appraisal of emerging tools.
  • Randomized controlled trials (RCTs) are essential for evaluating the long-term impact and methodological rigor of AI/ML interventions in older adults.

Purpose of the Study:

  • To identify and evaluate recent randomized controlled trials (RCTs) of AI/ML-based interventions in adults aged 65 years and older.
  • To assess the clinical impact, scalability, and methodological rigor of AI/ML interventions in geriatric care.
  • To synthesize evidence on the effectiveness of AI/ML in rehabilitation, preventive care, and remote monitoring for older adults.

Main Methods:

  • A structured MEDLINE search was conducted for RCTs published between January 2023 and December 2025.
  • Studies evaluated AI/ML interventions in adults aged 65 years and older.
  • Inclusion criteria focused on scalability, clinical impact, and methodological rigor, with four articles meeting the criteria.

Main Results:

  • AI/ML interventions demonstrated significant clinical benefits in postoperative rehabilitation and preventive care.
  • Applications included robotic systems for weight-bearing quantification, algorithm-guided rehabilitation, personalized exercise programs via smartphone platforms, and AI-driven vaccine counseling chatbots.
  • AI-driven interventions improved physical recovery, psychosocial well-being, and uptake of preventive measures compared to conventional care.

Conclusions:

  • AI/ML interventions offer transformative potential for enhancing geriatric care, improving recovery, and supporting well-being.
  • Future implementation requires addressing digital health literacy, ensuring long-term follow-up, and mitigating potential bias.
  • Prioritizing safety, ethics, and equitable access is essential to ensure AI enhances, rather than replaces, person-centered geriatric care.