Jul 2026· Journal of The American Geriatrics Society· 1 citation· 28 references
Medicine
TL;DR
GenAI should augment, not replace, clinical judgment and relational care, and 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.
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.
This study evaluates the accuracy of two of the foremost large language models (LLMs) used in clinical decision support tools for regional and neuraxial anesthesia procedures for patients receiving antithrombotic medications.
Conner M. Willson, Birpartap S. Thind, Jay Srinivas et al.· Cureus· 0 citations
Two short vignettes and a design guide to help human factors researchers create explanation systems that are practical and trustworthy are presented to show how explainability can become part of the care system instead of being treated as a separate technical feature.
T. Mamun, Laurie Novak, M. Salwei· Proceedings of the Internati...· 1 citation
A one-day summit was hosted in Washington, DC, to explore the future and potential uses of Artificial Intelligence (AI) for older adults and the challenges facing the implementation of this technology into the U.S. health care system.
Peter M. Abadir, Phillip H. C. Phan, M. J. R. Aliberti et al.· Journal of The American Geri...· 0 citations
Although promising, LLM-based systems are not yet reliable enough for autonomous medical diagnosis, and multiple recommendations for future research are contained to ensure a high level of safety, transparency, and clinical applicability for LLMs and other AI/ML-related technologies and devices.
M. U. K. Gunawardhna, Pirunthavi Wijikumar, D. Weerasinghe· Sri Lankan Journal of Applie...· 1 citation
This article proposes seven questions that clinicians can run through to evaluate any clinical AI tool in the time it takes to read an abstract, alongside a traffic-light schema for matching oversight to risk and a short list of demands clinicians should make of vendors and institutions.
Alaa Abdelqader, M. Alkhateeb, Abdullah Al-Marrawi et al.· Avicenna Journal of Medicine· 0 citations
A narrative conceptual review of four commonly encountered AI model types: random forests, support vector machines, convolutional neural networks and large language models is presented, exploring their clinical applications, interpretability, limitations and relevance to perioperative and surgical settings.
Kevin Patrick Cairns· BMJ Innovations· 0 citations
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