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

Abstract

Abstract Rehabilitation is a vital component of healthcare, 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 (LLMs) offers promising solutions to better understand this ‘black box’. LLMs 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'. LLMs enhance data integration, communication, assessment, and prediction. This paper delves into the ground-breaking use of LLMs as a tool to further understand the rehabilitation process. LLMs address current rehabilitation issues, including data bias, contextual comprehension, and ethical concerns. Collaboration with healthcare experts and rigorous validation is crucial when deploying LLMs. Integrating LLMs into rehabilitation yields insights into this intricate process, enhancing data-driven decision-making, refining clinical practices, and predicting rehabilitation outcomes. Although challenges persist, LLMs represent a significant stride in rehabilitation, underscoring the importance of ethical use and collaboration.