Generative AI in the care of older adults: A position statement from the American Geriatrics Society

Authors

• Ankur Bharija, CareCentrix, Tampa, Florida, USA.
• Ariba Khan, Advocate Health - MidwestFollow
• Juliessa M. Pavon, Division of Geriatrics, Duke University School of Medicine, Durham, North Carolina, USA.
• Ardeshir Z. Hashmi, Cleveland Clinic Center for Geriatric Medicine, Cleveland, Ohio, USA.
• Shivani K. Jindal, Department of Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
• Mark Dredze, Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
• Aruna V. Josyula, American Geriatrics Society, New York, New York, USA.
• William Hung, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
• Stephanie Nothelle, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
• Neela K. Patel, Division of Geriatrics and Supportive Care, Department of Family and Community Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
• Peter M. Abadir, School of Medicine and Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA.

Recommended Citation

Bharija A, Khan A, Pavon JM, et al. Generative AI in the Care of Older Adults: A Position Statement From the American Geriatrics Society. J Am Geriatr Soc. Published online July 21, 2026. doi:10.1111/jgs.70566

Abstract

Background: Generative artificial intelligence (GenAI), particularly large language models (LLMs), is being integrated into healthcare documentation, decision support, patient education, administrative workflows, and emerging agentic systems capable of initiating clinical and operational actions. While GenAI may reduce clinician burden and support person-centered care, it also introduces risks such as misinformation, algorithmic bias, privacy harms, errors of omission, and automation over-reliance. These risks may be amplified for older adults because core geriatrics care tasks, such as goals-of-care discussions, capacity-sensitive consent, polypharmacy and deprescribing, and functional and cognitive assessment in the setting of multimorbidity, may be underrepresented in training data and are high-stakes in practice.

Methods: The American Geriatrics Society (AGS) convened an interdisciplinary working and writing group, reviewed relevant literature and reports, incorporated input from multiple AGS committees, and completed review and approval through AGS committee processes in March 2026.

Results: This AGS position statement translates core geriatrics principles, person-centered care, equity, shared decision-making, and promoting independence into actionable recommendations for clinicians, health systems, developers, policymakers, older adults, and care partners. Recommendations address ethical use, clinical integration and oversight, transparency and documentation, privacy protections, governance across the AI lifecycle, including pre, during, and post-deployment monitoring and accountability, and priority geriatrics use cases.

Conclusion: GenAI should augment, not replace, clinical judgment and relational care. Responsible use in geriatrics requires transparency, clinician-in-the-loop oversight, validation in older adult populations using age-relevant outcomes, and governance safeguards to protect dignity, safety, and equity.

Type

Article

PubMed ID

42478489