Large Language Models' Responses to Spinal Cord Injury: A Comparative Study of Performance.
With the increasing application of large language models (LLMs) in the medical field, their potential in patient education and clinical decision support is becoming increasingly prominent. Given the complex pathogenesis, diverse treatment options, and lengthy rehabilitation periods of spinal cord in...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 15 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
3/25/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183971570&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183971570 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 3/25/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 183971570 183971570 183971570 10.1007/s10916-025-02170-7 183971570 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Large Language Models' Responses to Spinal Cord Injury: A Comparative Study of Performance. aug: au: Li, Jinze Chang, Chao Li, Yanqiu Cui, Shengyu Yuan, Fan Li, Zhuojun Wang, Xinyu Li, Kang Feng, Yuxin Wang, Zuowei Wei, Zhijian Jian, Fengzeng affil: https://ror.org/013xs5b60 Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, 100053, Beijing, China sug: subj: Natural Language Processing Artificial Intelligence, Generative Spinal Cord Injuries Pathology Spinal Cord Injuries Risk Factors Disease Attributes Spinal Cord Injuries Diagnosis Spinal Cord Injuries Therapy Spinal Cord Injuries Prognosis Funding Source Human Descriptive Statistics Data Analysis Software T-Tests Kruskal-Wallis Test Post Hoc Analysis Comparative Studies Patient Education Decision Making, Clinical ab: With the increasing application of large language models (LLMs) in the medical field, their potential in patient education and clinical decision support is becoming increasingly prominent. Given the complex pathogenesis, diverse treatment options, and lengthy rehabilitation periods of spinal cord injury (SCI), patients are increasingly turning to advanced online resources to obtain relevant medical information. This study analyzed responses from four LLMs—ChatGPT-4o, Claude-3.5 sonnet, Gemini-1.5 Pro, and Llama-3.1—to 37 SCI-related questions spanning pathogenesis, risk factors, clinical features, diagnostics, treatments, and prognosis. Quality and readability were assessed using the Ensuring Quality Information for Patients (EQIP) tool and Flesch-Kincaid metrics, respectively. Accuracy was independently scored by three senior spine surgeons using consensus scoring. Performance varied among the models. Gemini ranked highest in EQIP scores, suggesting superior information quality. Although the readability of all four LLMs was generally low, requiring a college-level reading comprehension ability, they were all able to effectively simplify complex content. Notably, ChatGPT led in accuracy, achieving significantly higher "Good" ratings (83.8%) compared to Claude (78.4%), Gemini (54.1%), and Llama (62.2%). Comprehensiveness scores were high across all models. Furthermore, the LLMs exhibited strong self-correction abilities. After being prompted for revision, the accuracy of ChatGPT and Claude's responses improved by 100% and 50%, respectively; both Gemini and Llama improved by 67%. This study represents the first systematic comparison of leading LLMs in the context of SCI. While Gemini excelled in response quality, ChatGPT provided the most accurate and comprehensive responses. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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