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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.
Afiliação
  • Lahat A; Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel Aviv University, Tel Aviv, Israel. zokadi@gmail.com.
  • Shachar E; Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel Aviv University, Tel Aviv, Israel.
  • Avidan B; Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel Aviv University, Tel Aviv, Israel.
  • Shatz Z; Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel Aviv University, Tel Aviv, Israel.
  • Glicksberg BS; Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Klang E; The Sami Sagol AI Hub, ARC Innovation Center, Chaim Sheba Medical Center, Affiliated to Tel-Aviv University, Tel Aviv, Israel.
Sci Rep ; 13(1): 4164, 2023 03 13.
Article em En | MEDLINE | ID: mdl-36914821
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Inflamatórias Intestinais / Gastroenterologistas / Gastroenterologia Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Israel País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Inflamatórias Intestinais / Gastroenterologistas / Gastroenterologia Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Israel País de publicação: Reino Unido