Leveraging Multimodal Large Language Models for Fall Risk Reduction in Older Adults in the Home: Proposed Model Design

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not quantified

• Primary Outcome: Demonstration that multimodal LLMs can generate safe and actionable fall prevention advice

• Nnt Or Sensitivity: Not applicable - proof-of-concept study

• Confidence Interval: Not provided

Clinical Application

Requires further validation before clinical implementation; technology accessibility concerns for older adults Relevant to Australian aged care and community health services; would need TGA evaluation for medical device classification; aligns with RACGP falls prevention guidelines emphasis on home safety Community-dwelling older adults with fall risk factors

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.

References

1. 1.Do, J., Suresh, V., Zhang, L., Chavre, B. M., Cha, J., & Pugliese, R. (2026). Leveraging Multimodal Large Language Models for Fall Risk Reduction in Older Adults in the Home: Proposed Model Design. JMIR Aging. https://doi.org/10.2196/77591