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评估大型语言模型在识别胃肠病学领域顶级研究问题中的应用。

Evaluating the use of large language model in identifying top research questions in gastroenterology.

机构信息

Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel Aviv University, Tel Aviv, Israel.

Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

出版信息

Sci Rep. 2023 Mar 13;13(1):4164. doi: 10.1038/s41598-023-31412-2.

Abstract

The field of gastroenterology (GI) is constantly evolving. It is essential to pinpoint the most pressing and important research questions. To evaluate the potential of chatGPT for identifying research priorities in GI and provide a starting point for further investigation. We queried chatGPT on four key topics in GI: inflammatory bowel disease, microbiome, Artificial Intelligence in GI, and advanced endoscopy in GI. A panel of experienced gastroenterologists separately reviewed and rated the generated research questions on a scale of 1-5, with 5 being the most important and relevant to current research in GI. chatGPT generated relevant and clear research questions. Yet, the questions were not considered original by the panel of gastroenterologists. On average, the questions were rated 3.6 ± 1.4, with inter-rater reliability ranging from 0.80 to 0.98 (p < 0.001). The mean grades for relevance, clarity, specificity, and originality were 4.9 ± 0.1, 4.6 ± 0.4, 3.1 ± 0.2, 1.5 ± 0.4, respectively. Our study suggests that Large Language Models (LLMs) may be a useful tool for identifying research priorities in the field of GI, but more work is needed to improve the novelty of the generated research questions.

摘要

胃肠病学(GI)领域在不断发展。确定最紧迫和最重要的研究问题至关重要。为了评估 chatGPT 在确定 GI 研究重点方面的潜力,并为进一步研究提供起点。我们就 GI 中的四个关键主题向 chatGPT 提问:炎症性肠病、微生物组、GI 中的人工智能和 GI 中的高级内镜。一组经验丰富的胃肠病学家分别对生成的研究问题进行了 1-5 分的评估,5 分表示与 GI 中的当前研究最相关和最重要。chatGPT 生成了相关且明确的研究问题。然而,这些问题并没有被胃肠病学家小组认为是原创的。平均而言,这些问题的评分是 3.6±1.4,组内评分者之间的可靠性从 0.80 到 0.98(p<0.001)。相关性、清晰度、特异性和新颖性的平均分数分别为 4.9±0.1、4.6±0.4、3.1±0.2、1.5±0.4。我们的研究表明,大型语言模型(LLM)可能是确定 GI 领域研究重点的有用工具,但需要做更多的工作来提高生成研究问题的新颖性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0467/10011374/f273d3a33cd1/41598_2023_31412_Fig1_HTML.jpg

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