Department of Rehabilitation Medicine, Kyorin University School of Medicine, Japan.
Department of Rehabilitation Medicine, Kyorin University School of Medicine, Japan; Department of Rehabilitation Medicine, Keio University School of Medicine, Japan.
J Rehabil Med. 2023 Sep 11;55:jrm13373. doi: 10.2340/jrm.v55.13373.
To explore the potential use of artificial intelligence language models in formulating rehabilitation prescriptions and International Classification of Functioning, Disability and Health (ICF) codes. Design: Comparative study based on a single case report compared to standard answers from a textbook.
A stroke case from textbook. Methods: Chat Generative Pre-Trained Transformer-4 (ChatGPT-4)was used to generate comprehensive medical and rehabilitation prescription information and ICF codes pertaining to the stroke case. This information was compared with standard answers from textbook, and 2 licensed Physical Medicine and Rehabilitation (PMR) clinicians reviewed the artificial intelligence recommendations for further discussion.
ChatGPT-4 effectively formulated rehabilitation prescriptions and ICF codes for a typical stroke case, together with a rationale to support its recommendations. This information was generated in seconds. Compared with standard answers, the large language model generated broader and more general prescriptions in terms of medical problems and management plans, rehabilitation problems and management plans, as well as rehabilitation goals. It also demonstrated the ability to propose specified approaches for each rehabilitation therapy. The language model made an error regarding the ICF category for the stroke case, but no mistakes were identified in the ICF codes assigned. Conclusion: This test case suggests that artificial intelligence language models have potential use in facilitating clinical practice and education in the field of rehabilitation medicine.
探索人工智能语言模型在制定康复处方和国际功能、残疾和健康分类（ICF）编码方面的潜在用途。
基于单个病例报告的对比研究，与教科书的标准答案进行比较。
来自教科书的中风病例。
使用 Chat Generative Pre-Trained Transformer-4（ChatGPT-4）生成与中风病例相关的全面医疗和康复处方信息以及 ICF 编码。将这些信息与教科书的标准答案进行比较，由 2 名持照物理医学与康复（PMR）临床医生对人工智能推荐意见进行审查，以便进一步讨论。
ChatGPT-4 有效地为典型中风病例制定了康复处方和 ICF 编码，并提供了支持其建议的基本原理。这些信息是在几秒钟内生成的。与标准答案相比，大型语言模型在医疗问题和管理计划、康复问题和管理计划以及康复目标方面生成了更广泛和更一般的处方。它还展示了为每个康复治疗方法提出具体方法的能力。语言模型在中风病例的 ICF 类别方面犯了一个错误，但分配的 ICF 编码没有错误。
本案例研究表明，人工智能语言模型在康复医学领域的临床实践和教育中具有潜在的应用价值。
