{"title":"Lumbar Radicular Pain in the Eyes of Artificial Intelligence: Can You 'Imagine' What I 'Feel'?","authors":"Mustafa Hüseyin Temel, Yakup Erden, Fatih Bağcıer","doi":"10.1016/j.wneu.2024.09.075","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>Pain is a complex sensory and emotional experience that significantly impacts individuals' well-being. Lumbar radicular pain (LRP) is a prevalent neuropathic pain affecting 9.9% to 25% of the population annually. Accurate identification of pain patterns in LRP is essential for diagnosis and management. Artificial intelligence has potential in healthcare but faces challenges in reliably and accuracy. This study aimed to investigate the accuracy and consistency of LRP patterns demonstrated by ChatGPT-4o.</p><p><strong>Methods: </strong>The study was conducted at Üsküdar State Hospital from June 1 to June 30, 2024, utilizing the GPT-4o language model. ChatGPT-4o was prompted to generate and mark LRP patterns for L4, L5, and S1 radiculopathies on an anatomical model. The process was repeated after two weeks to assess consistency. The markings by ChatGPT were compared with those by two experienced specialists using OpenCV for analysis. Statistical tests, including the Mann-Whitney U-Test, were conducted.</p><p><strong>Results: </strong>ChatGPT's initial and follow-up markings of L4, L5, and S1 radiculopathy pain patterns were statistically significantly different from each other and from the specialists' markings (p<0.001 for all comparisons).</p><p><strong>Conclusion: </strong>ChatGPT currently lacks the capacity to accurately and consistently represent LRP patterns. AI tools in healthcare require further refinement, validation, and regulation to ensure reliability and safety. Future research should involve multiple AI platforms and broader medical conditions to enhance generalizability.</p>","PeriodicalId":23906,"journal":{"name":"World neurosurgery","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World neurosurgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.wneu.2024.09.075","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Aim: Pain is a complex sensory and emotional experience that significantly impacts individuals' well-being. Lumbar radicular pain (LRP) is a prevalent neuropathic pain affecting 9.9% to 25% of the population annually. Accurate identification of pain patterns in LRP is essential for diagnosis and management. Artificial intelligence has potential in healthcare but faces challenges in reliably and accuracy. This study aimed to investigate the accuracy and consistency of LRP patterns demonstrated by ChatGPT-4o.
Methods: The study was conducted at Üsküdar State Hospital from June 1 to June 30, 2024, utilizing the GPT-4o language model. ChatGPT-4o was prompted to generate and mark LRP patterns for L4, L5, and S1 radiculopathies on an anatomical model. The process was repeated after two weeks to assess consistency. The markings by ChatGPT were compared with those by two experienced specialists using OpenCV for analysis. Statistical tests, including the Mann-Whitney U-Test, were conducted.
Results: ChatGPT's initial and follow-up markings of L4, L5, and S1 radiculopathy pain patterns were statistically significantly different from each other and from the specialists' markings (p<0.001 for all comparisons).
Conclusion: ChatGPT currently lacks the capacity to accurately and consistently represent LRP patterns. AI tools in healthcare require further refinement, validation, and regulation to ensure reliability and safety. Future research should involve multiple AI platforms and broader medical conditions to enhance generalizability.
期刊介绍：
World Neurosurgery has an open access mirror journal World Neurosurgery: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review.
The journal''s mission is to:
-To provide a first-class international forum and a 2-way conduit for dialogue that is relevant to neurosurgeons and providers who care for neurosurgery patients. The categories of the exchanged information include clinical and basic science, as well as global information that provide social, political, educational, economic, cultural or societal insights and knowledge that are of significance and relevance to worldwide neurosurgery patient care.
-To act as a primary intellectual catalyst for the stimulation of creativity, the creation of new knowledge, and the enhancement of quality neurosurgical care worldwide.
-To provide a forum for communication that enriches the lives of all neurosurgeons and their colleagues; and, in so doing, enriches the lives of their patients.
Topics to be addressed in World Neurosurgery include: EDUCATION, ECONOMICS, RESEARCH, POLITICS, HISTORY, CULTURE, CLINICAL SCIENCE, LABORATORY SCIENCE, TECHNOLOGY, OPERATIVE TECHNIQUES, CLINICAL IMAGES, VIDEOS
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