English

Physio: An LLM-Based Physiotherapy Advisor

Computation and Language 2024-05-02 v1 Information Retrieval

Abstract

The capabilities of the most recent language models have increased the interest in integrating them into real-world applications. However, the fact that these models generate plausible, yet incorrect text poses a constraint when considering their use in several domains. Healthcare is a prime example of a domain where text-generative trustworthiness is a hard requirement to safeguard patient well-being. In this paper, we present Physio, a chat-based application for physical rehabilitation. Physio is capable of making an initial diagnosis while citing reliable health sources to support the information provided. Furthermore, drawing upon external knowledge databases, Physio can recommend rehabilitation exercises and over-the-counter medication for symptom relief. By combining these features, Physio can leverage the power of generative models for language processing while also conditioning its response on dependable and verifiable sources. A live demo of Physio is available at https://physio.inesctec.pt.

Keywords

Cite

@article{arxiv.2401.01825,
  title  = {Physio: An LLM-Based Physiotherapy Advisor},
  author = {Rúben Almeida and Hugo Sousa and Luís F. Cunha and Nuno Guimarães and Ricardo Campos and Alípio Jorge},
  journal= {arXiv preprint arXiv:2401.01825},
  year   = {2024}
}

Comments

Demo, ECIR 2024, 3rd Sword AI challenge 2023

R2 v1 2026-06-28T14:07:56.923Z