Physio: An LLM-Based Physiotherapy Advisor
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917655015849984 |
|---|---|
| author | Almeida, Rúben Sousa, Hugo Cunha, Luís F. Guimarães, Nuno Campos, Ricardo Jorge, Alípio |
| author_facet | Almeida, Rúben Sousa, Hugo Cunha, Luís F. Guimarães, Nuno Campos, Ricardo Jorge, Alípio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physio: An LLM-Based Physiotherapy Advisor Almeida, Rúben Sousa, Hugo Cunha, Luís F. Guimarães, Nuno Campos, Ricardo Jorge, Alípio Computation and Language Information Retrieval 68T07 I.2; J.3 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. |
| title | Physio: An LLM-Based Physiotherapy Advisor |
| topic | Computation and Language Information Retrieval 68T07 I.2; J.3 |
| url | https://arxiv.org/abs/2401.01825 |