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Unlocking the Black Box? A Comprehensive Exploration of Large Language Models in Rehabilitation.

Authors :
Bonnechère, Bruno
Source :
American Journal of Physical Medicine & Rehabilitation. Jun2024, Vol. 103 Issue 6, p532-537. 6p.
Publication Year :
2024

Abstract

Rehabilitation is a vital component of health care, aiming to restore function and improve the well-being of individuals with disabilities or injuries. Nevertheless, the rehabilitation process is often likened to a "black box," with complexities that pose challenges for comprehensive analysis and optimization. The emergence of large language models offers promising solutions to better understand this "black box." Large language models excel at comprehending and generating human-like text, making them valuable in the healthcare sector. In rehabilitation, healthcare professionals must integrate a wide range of data to create effective treatment plans, akin to selecting the best ingredients for the "black box." Large language models enhance data integration, communication, assessment, and prediction. This article delves into the ground-breaking use of large language models as a tool to further understand the rehabilitation process. Large language models address current rehabilitation issues, including data bias, contextual comprehension, and ethical concerns. Collaboration with healthcare experts and rigorous validation is crucialwhen deploying large language models. Integrating large language models into rehabilitation yields insights into this intricate process, enhancing data-driven decision making, refining clinical practices, and predicting rehabilitation outcomes. Although challenges persist, large language models represent a significant stride in rehabilitation, underscoring the importance of ethical use and collaboration. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08949115
Volume :
103
Issue :
6
Database :
Academic Search Index
Journal :
American Journal of Physical Medicine & Rehabilitation
Publication Type :
Academic Journal
Accession number :
177979035
Full Text :
https://doi.org/10.1097/PHM.0000000000002440