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

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 crucial when 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.