Artificial intelligence versus clinical judgement: how accurately do generative models reflect CNS guidelines for chiari malformation?

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY Clinical Neurology and Neurosurgery Pub Date : 2025-01-01 Epub Date: 2024-11-26 DOI:10.1016/j.clineuro.2024.108662
David Shin , Hyunah Park , Isabel Shaffrey , Vahe Yacoubian , Taha M. Taka , Justin Dye , Olumide Danisa
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Abstract

Objective

This study investigated the response and readability of generative artificial intelligence (AI) models to questions and recommendations proposed by the 2023 Congress of Neurological Surgeons (CNS) guidelines for Chiari 1 malformation.

Methods

Thirteen questions were generated from CNS guidelines and asked to Perplexity, ChatGPT 4o, Microsoft Copilot, and Google Gemini. AI answers were divided into two categories, "concordant" and "non-concordant," according to their alignment with current CNS guidelines. Non-concordant answers were sub-categorized as “insufficient” or “over-conclusive.” Responses were evaluated for readability via the Flesch-Kincaid Grade Level, Gunning Fog Index, SMOG (Simple Measure of Gobbledygook) Index, and Flesch Reading Ease test.

Results

Perplexity displayed the highest concordance rate of 69.2 %, with non-concordant responses classified as 0 % insufficient and 30.8 % over-conclusive. ChatGPT 4o had the lowest concordance rate at 23.1 %, with 0 % insufficient and 76.9 % over-conclusive classifications. Copilot showed a 61.5 % concordance rate, with 7.7 % insufficient and 30.8 % over-conclusive. Gemini demonstrated a 30.8 % concordance rate, with 7.7 % insufficient and 61.5 % as over-conclusive. Flesch-Kincaid Grade Level scores ranged from 14.48 (Gemini) to 16.48 (Copilot), Gunning Fog Index scores varied between 16.18 (Gemini) and 18.8 (Copilot), SMOG Index scores ranged from 16 (Gemini) to 17.54 (Copilot), and Flesch Reading Ease scores were low across all models, with Gemini showing the highest mean score of 21.3.

Conclusion

Perplexity and Copilot emerged as the best-performing for concordance, while ChatGPT and Gemini displayed the highest over-conclusive rates. All responses showcased high complexity and difficult readability. While AI can be valuable in certain aspects of clinical practice, the low concordance rates show that AI should not replace clinician judgement.
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人工智能与临床判断:生成模型如何准确地反映中枢神经系统对基亚里畸形的指导?
目的探讨生成式人工智能(AI)模型对2023年神经外科医师大会(CNS) Chiari 1型畸形指南中提出的问题和建议的反应和可读性。方法从CNS指南中生成13个问题,并向Perplexity、ChatGPT 40、Microsoft Copilot和谷歌Gemini提问。人工智能的答案根据与当前中枢神经系统指南的一致性分为“一致”和“不一致”两类。不一致的答案被分类为“不充分”或“过度结论性”。通过Flesch- kinkaid等级水平、射击雾指数、SMOG(简单测量的官样书)指数和Flesch阅读难度测试来评估回复的可读性。结果调查结果的一致性率最高,为69.2 %,不一致性反应为0 %,不充分反应为30.8 %。ChatGPT 40的一致性率最低,为23.1% %,0 %分类不充分,76.9% %分类过度。副驾驶显示61.5 %的一致性率,7.7 %的不充分和30.8 %的过度结论。Gemini的一致性率为30.8 %,不足率为7.7 %,过度结论率为61.5 %。Flesch- kincaid Grade Level得分从14.48(双子星)到16.48(副驾驶)不等,射击雾指数得分从16.18(双子星)到18.8(副驾驶)不等,烟雾指数得分从16(双子星)到17.54(副驾驶)不等,Flesch Reading Ease得分在所有型号中都很低,其中双子星的平均得分最高,为21.3。结论perplexity和Copilot在一致性方面表现最好,而ChatGPT和Gemini表现出最高的过度结论性。所有的回答都显示出高度的复杂性和难读性。虽然人工智能在临床实践的某些方面是有价值的,但低一致性率表明人工智能不应该取代临床医生的判断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Clinical Neurology and Neurosurgery
Clinical Neurology and Neurosurgery 医学-临床神经学
CiteScore
3.70
自引率
5.30%
发文量
358
审稿时长
46 days
期刊介绍: Clinical Neurology and Neurosurgery is devoted to publishing papers and reports on the clinical aspects of neurology and neurosurgery. It is an international forum for papers of high scientific standard that are of interest to Neurologists and Neurosurgeons world-wide.
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