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Physio: An LLM-Based Physiotherapy Advisor

arXiv 2401.01825 ↗First posted 3 Jan 2024cs.CL cs.IRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 3 Jan 2024.

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References · 6

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  4. 2023Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Textscited by 33
  5. 2023Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?cited by 31
  6. 2023Citation: A Key to Building Responsible and Accountable Large Language Modelscited by 20

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