Document Type
Article
Publication Date
5-13-2026
Abstract
This research letter proposes a novel model design leveraging natively multimodal large language models to identify fall risks and generate visualizations of recommended home environmental modifications, aiming to improve the accessibility and impact of personalized fall prevention advice for older adults. Through a pilot rating study, this work demonstrates that multimodal large language models can generate safe and actionable advice to reduce fall risk in lived spaces of older adults, and also generate realistic edits based on original images. While this concept needs further testing and clinical comparison, it highlights a promising avenue for further innovation of fall prevention tactics.
Recommended Citation
Do, Justin; Suresh, Vivaswat; Zhang, Lily; Chavre, Bharvi M.; Cha, Jeremy; and Pugliese, Robert S., "Leveraging Multimodal Large Language Models for Fall Risk Reduction in Older Adults in the Home: Proposed Model Design" (2026). SKMC Student Presentations and Publications. Paper 88.
https://jdc.jefferson.edu/skmcstudentworks/88
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Language
English
Comments
This article is the author's final published version in JMIR Aging, Volume 9, 2026, Article Number e77591.
The published version is available at https://doi.org/10.2196/77591. Copyright © Justin Do, Vivaswat Suresh, Lily Zhang, Bharvi M Chavre, Jeremy Cha, Robert Pugliese.