Department of Orthopaedics, Peking University Third Hospital, Beijing, China.
Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, China.
J Med Internet Res. 2024 Nov 15;26:e59607. doi: 10.2196/59607.
Large language models (LLMs) can understand natural language and generate corresponding text, images, and even videos based on prompts, which holds great potential in medical scenarios. Orthopedics is a significant branch of medicine, and orthopedic diseases contribute to a significant socioeconomic burden, which could be alleviated by the application of LLMs. Several pioneers in orthopedics have conducted research on LLMs across various subspecialties to explore their performance in addressing different issues. However, there are currently few reviews and summaries of these studies, and a systematic summary of existing research is absent.
The objective of this review was to comprehensively summarize research findings on the application of LLMs in the field of orthopedics and explore the potential opportunities and challenges.
PubMed, Embase, and Cochrane Library databases were searched from January 1, 2014, to February 22, 2024, with the language limited to English. The terms, which included variants of "large language model," "generative artificial intelligence," "ChatGPT," and "orthopaedics," were divided into 2 categories: large language model and orthopedics. After completing the search, the study selection process was conducted according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the revised Cochrane risk-of-bias tool for randomized trials and CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) guidance. Data extraction and synthesis were conducted after the quality assessment.
A total of 68 studies were selected. The application of LLMs in orthopedics involved the fields of clinical practice, education, research, and management. Of these 68 studies, 47 (69%) focused on clinical practice, 12 (18%) addressed orthopedic education, 8 (12%) were related to scientific research, and 1 (1%) pertained to the field of management. Of the 68 studies, only 8 (12%) recruited patients, and only 1 (1%) was a high-quality randomized controlled trial. ChatGPT was the most commonly mentioned LLM tool. There was considerable heterogeneity in the definition, measurement, and evaluation of the LLMs' performance across the different studies. For diagnostic tasks alone, the accuracy ranged from 55% to 93%. When performing disease classification tasks, ChatGPT with GPT-4's accuracy ranged from 2% to 100%. With regard to answering questions in orthopedic examinations, the scores ranged from 45% to 73.6% due to differences in models and test selections.
LLMs cannot replace orthopedic professionals in the short term. However, using LLMs as copilots could be a potential approach to effectively enhance work efficiency at present. More high-quality clinical trials are needed in the future, aiming to identify optimal applications of LLMs and advance orthopedics toward higher efficiency and precision.
大型语言模型（LLM）能够理解自然语言，并根据提示生成相应的文本、图像，甚至视频，这在医学领域具有巨大的潜力。骨科是医学的一个重要分支，骨科疾病造成了巨大的社会经济负担，而 LLM 的应用可能会缓解这种负担。一些骨科先驱已经在各个亚专业领域对 LLM 进行了研究，以探索其在解决不同问题方面的性能。然而，目前针对这些研究的综述和总结较少，缺乏对现有研究的系统总结。
本综述旨在全面总结 LLM 在骨科领域的应用研究结果，并探讨其潜在的机遇和挑战。
检索了 2014 年 1 月 1 日至 2024 年 2 月 22 日的 PubMed、Embase 和 Cochrane Library 数据库，语言限制为英语。将包括“大型语言模型”、“生成式人工智能”、“ChatGPT”和“骨科”等变体的术语分为 2 类：大型语言模型和骨科。完成搜索后，根据纳入和排除标准进行研究选择过程。使用修订后的 Cochrane 随机试验偏倚风险工具和 CONSORT-AI（临床试验人工智能报告标准）指南评估纳入研究的质量。在质量评估后进行数据提取和综合。
共选择了 68 项研究。LLM 在骨科中的应用涉及临床实践、教育、研究和管理领域。在这 68 项研究中，47 项（69%）侧重于临床实践，12 项（18%）涉及骨科教育，8 项（12%）与科学研究有关，1 项（1%）涉及管理领域。在这 68 项研究中，只有 8 项（12%）招募了患者，只有 1 项（1%）是高质量的随机对照试验。ChatGPT 是最常被提及的 LLM 工具。不同研究之间，LLM 性能的定义、测量和评估存在相当大的异质性。仅在诊断任务方面，准确性范围为 55%至 93%。在进行疾病分类任务时，ChatGPT 与 GPT-4 的准确性范围为 2%至 100%。在回答骨科检查中的问题时，由于模型和测试选择的差异，分数范围为 45%至 73.6%。
在短期内，LLM 不能替代骨科专业人员。然而，目前使用 LLM 作为副驾可能是一种提高工作效率的潜在方法。未来需要更多高质量的临床试验，以确定 LLM 的最佳应用，并推动骨科朝着更高效率和精度的方向发展。
