ChatGPT和人类评估人员根据公认的报告标准评估医学文献的比较研究。

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Richard Hr Roberts, Stephen R Ali, Hayley A Hutchings, Thomas D Dobbs, Iain S Whitaker
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引用次数: 0

摘要

引言:在临床医生保持医学研究最新进展的挑战中,人工智能(AI)工具,如大型语言模型(LLM)ChatGPT,可以自动评估研究质量,节省时间并减少偏见。这项研究将ChatGPT3的熟练程度与人类对摘要评分的评估进行了比较,以确定其作为证据合成工具的潜力。方法:我们将ChatGPT对种植牙摘要的评分与使用摘要报告标准清单的综合报告试验标准的人类评估者进行了比较,得出总体依从性评分(OCS)。Bland-Altman分析评估了人类和人工智能生成的OCS百分比之间的一致性。额外的误差分析包括OCS分量表的平均差、Welch t检验和Pearson相关系数。结果:Bland-Altman分析显示,人类评估和ChatGPT之间的OCS平均差异为4.92%(95%CI 0.62%,0.37%)。误差分析在大多数领域中显示出较小的平均差异,在“结论”中最高(0.764(95%CI 0.186,0.280),在“致盲”中最低(0.034(95%CI 0.818/0.895))(r=0.32,p结论:像ChatGPT这样的LLM可以帮助自动化医学文献的评估,有助于识别准确报告的研究。ChatGPT的可能应用包括在医学数据库中集成以进行抽象评估。目前的限制包括令牌限制,将其使用限制在摘要上。随着人工智能技术的进步,像GPT4这样的未来版本可能会关闭er更可靠、全面的评估,加强高质量研究的识别,并有可能改善患者的预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards.

Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards.

Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards.
Introduction Amid clinicians’ challenges in staying updated with medical research, artificial intelligence (AI) tools like the large language model (LLM) ChatGPT could automate appraisal of research quality, saving time and reducing bias. This study compares the proficiency of ChatGPT3 against human evaluation in scoring abstracts to determine its potential as a tool for evidence synthesis. Methods We compared ChatGPT’s scoring of implant dentistry abstracts with human evaluators using the Consolidated Standards of Reporting Trials for Abstracts reporting standards checklist, yielding an overall compliance score (OCS). Bland-Altman analysis assessed agreement between human and AI-generated OCS percentages. Additional error analysis included mean difference of OCS subscores, Welch’s t-test and Pearson’s correlation coefficient. Results Bland-Altman analysis showed a mean difference of 4.92% (95% CI 0.62%, 0.37%) in OCS between human evaluation and ChatGPT. Error analysis displayed small mean differences in most domains, with the highest in ‘conclusion’ (0.764 (95% CI 0.186, 0.280)) and the lowest in ‘blinding’ (0.034 (95% CI 0.818, 0.895)). The strongest correlations between were in ‘harms’ (r=0.32, p<0.001) and ‘trial registration’ (r=0.34, p=0.002), whereas the weakest were in ‘intervention’ (r=0.02, p<0.001) and ‘objective’ (r=0.06, p<0.001). Conclusion LLMs like ChatGPT can help automate appraisal of medical literature, aiding in the identification of accurately reported research. Possible applications of ChatGPT include integration within medical databases for abstract evaluation. Current limitations include the token limit, restricting its usage to abstracts. As AI technology advances, future versions like GPT4 could offer more reliable, comprehensive evaluations, enhancing the identification of high-quality research and potentially improving patient outcomes.
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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