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Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications
Literature reviews are valuable for summarizing and evaluating the available evidence in various medical fields, including nephrology. However, identifying and exploring the potential sources requires focus and time devoted to literature searching for clinicians and researchers. ChatGPT is a novel a...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488525/ https://www.ncbi.nlm.nih.gov/pubmed/37685617 http://dx.doi.org/10.3390/jcm12175550 |
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author | Suppadungsuk, Supawadee Thongprayoon, Charat Krisanapan, Pajaree Tangpanithandee, Supawit Garcia Valencia, Oscar Miao, Jing Mekraksakit, Poemlarp Kashani, Kianoush Cheungpasitporn, Wisit |
author_facet | Suppadungsuk, Supawadee Thongprayoon, Charat Krisanapan, Pajaree Tangpanithandee, Supawit Garcia Valencia, Oscar Miao, Jing Mekraksakit, Poemlarp Kashani, Kianoush Cheungpasitporn, Wisit |
author_sort | Suppadungsuk, Supawadee |
collection | PubMed |
description | Literature reviews are valuable for summarizing and evaluating the available evidence in various medical fields, including nephrology. However, identifying and exploring the potential sources requires focus and time devoted to literature searching for clinicians and researchers. ChatGPT is a novel artificial intelligence (AI) large language model (LLM) renowned for its exceptional ability to generate human-like responses across various tasks. However, whether ChatGPT can effectively assist medical professionals in identifying relevant literature is unclear. Therefore, this study aimed to assess the effectiveness of ChatGPT in identifying references to literature reviews in nephrology. We keyed the prompt “Please provide the references in Vancouver style and their links in recent literature on… name of the topic” into ChatGPT-3.5 (03/23 Version). We selected all the results provided by ChatGPT and assessed them for existence, relevance, and author/link correctness. We recorded each resource’s citations, authors, title, journal name, publication year, digital object identifier (DOI), and link. The relevance and correctness of each resource were verified by searching on Google Scholar. Of the total 610 references in the nephrology literature, only 378 (62%) of the references provided by ChatGPT existed, while 31% were fabricated, and 7% of citations were incomplete references. Notably, only 122 (20%) of references were authentic. Additionally, 256 (68%) of the links in the references were found to be incorrect, and the DOI was inaccurate in 206 (54%) of the references. Moreover, among those with a link provided, the link was correct in only 20% of cases, and 3% of the references were irrelevant. Notably, an analysis of specific topics in electrolyte, hemodialysis, and kidney stones found that >60% of the references were inaccurate or misleading, with less reliable authorship and links provided by ChatGPT. Based on our findings, the use of ChatGPT as a sole resource for identifying references to literature reviews in nephrology is not recommended. Future studies could explore ways to improve AI language models’ performance in identifying relevant nephrology literature. |
format | Online Article Text |
id | pubmed-10488525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104885252023-09-09 Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications Suppadungsuk, Supawadee Thongprayoon, Charat Krisanapan, Pajaree Tangpanithandee, Supawit Garcia Valencia, Oscar Miao, Jing Mekraksakit, Poemlarp Kashani, Kianoush Cheungpasitporn, Wisit J Clin Med Article Literature reviews are valuable for summarizing and evaluating the available evidence in various medical fields, including nephrology. However, identifying and exploring the potential sources requires focus and time devoted to literature searching for clinicians and researchers. ChatGPT is a novel artificial intelligence (AI) large language model (LLM) renowned for its exceptional ability to generate human-like responses across various tasks. However, whether ChatGPT can effectively assist medical professionals in identifying relevant literature is unclear. Therefore, this study aimed to assess the effectiveness of ChatGPT in identifying references to literature reviews in nephrology. We keyed the prompt “Please provide the references in Vancouver style and their links in recent literature on… name of the topic” into ChatGPT-3.5 (03/23 Version). We selected all the results provided by ChatGPT and assessed them for existence, relevance, and author/link correctness. We recorded each resource’s citations, authors, title, journal name, publication year, digital object identifier (DOI), and link. The relevance and correctness of each resource were verified by searching on Google Scholar. Of the total 610 references in the nephrology literature, only 378 (62%) of the references provided by ChatGPT existed, while 31% were fabricated, and 7% of citations were incomplete references. Notably, only 122 (20%) of references were authentic. Additionally, 256 (68%) of the links in the references were found to be incorrect, and the DOI was inaccurate in 206 (54%) of the references. Moreover, among those with a link provided, the link was correct in only 20% of cases, and 3% of the references were irrelevant. Notably, an analysis of specific topics in electrolyte, hemodialysis, and kidney stones found that >60% of the references were inaccurate or misleading, with less reliable authorship and links provided by ChatGPT. Based on our findings, the use of ChatGPT as a sole resource for identifying references to literature reviews in nephrology is not recommended. Future studies could explore ways to improve AI language models’ performance in identifying relevant nephrology literature. MDPI 2023-08-25 /pmc/articles/PMC10488525/ /pubmed/37685617 http://dx.doi.org/10.3390/jcm12175550 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Suppadungsuk, Supawadee Thongprayoon, Charat Krisanapan, Pajaree Tangpanithandee, Supawit Garcia Valencia, Oscar Miao, Jing Mekraksakit, Poemlarp Kashani, Kianoush Cheungpasitporn, Wisit Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications |
title | Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications |
title_full | Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications |
title_fullStr | Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications |
title_full_unstemmed | Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications |
title_short | Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications |
title_sort | examining the validity of chatgpt in identifying relevant nephrology literature: findings and implications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488525/ https://www.ncbi.nlm.nih.gov/pubmed/37685617 http://dx.doi.org/10.3390/jcm12175550 |
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