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Evaluating the use of large language model in identifying top research questions in gastroenterology
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011374/ https://www.ncbi.nlm.nih.gov/pubmed/36914821 http://dx.doi.org/10.1038/s41598-023-31412-2 |
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author | Lahat, Adi Shachar, Eyal Avidan, Benjamin Shatz, Zina Glicksberg, Benjamin S. Klang, Eyal |
author_facet | Lahat, Adi Shachar, Eyal Avidan, Benjamin Shatz, Zina Glicksberg, Benjamin S. Klang, Eyal |
author_sort | Lahat, Adi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10011374 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100113742023-03-15 Evaluating the use of large language model in identifying top research questions in gastroenterology Lahat, Adi Shachar, Eyal Avidan, Benjamin Shatz, Zina Glicksberg, Benjamin S. Klang, Eyal Sci Rep Article 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. Nature Publishing Group UK 2023-03-13 /pmc/articles/PMC10011374/ /pubmed/36914821 http://dx.doi.org/10.1038/s41598-023-31412-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lahat, Adi Shachar, Eyal Avidan, Benjamin Shatz, Zina Glicksberg, Benjamin S. Klang, Eyal Evaluating the use of large language model in identifying top research questions in gastroenterology |
title | Evaluating the use of large language model in identifying top research questions in gastroenterology |
title_full | Evaluating the use of large language model in identifying top research questions in gastroenterology |
title_fullStr | Evaluating the use of large language model in identifying top research questions in gastroenterology |
title_full_unstemmed | Evaluating the use of large language model in identifying top research questions in gastroenterology |
title_short | Evaluating the use of large language model in identifying top research questions in gastroenterology |
title_sort | evaluating the use of large language model in identifying top research questions in gastroenterology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011374/ https://www.ncbi.nlm.nih.gov/pubmed/36914821 http://dx.doi.org/10.1038/s41598-023-31412-2 |
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