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Assessing ChatGPT’s capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis

The application of large language models in clinical decision support (CDS) is an area that warrants further investigation. ChatGPT, a prominent large language models developed by OpenAI, has shown promising performance across various domains. However, there is limited research evaluating its use sp...

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Autores principales: Kao, Hsu-Ju, Chien, Tsair-Wei, Wang, Wen-Chung, Chou, Willy, Chow, Julie Chi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289633/
https://www.ncbi.nlm.nih.gov/pubmed/37352054
http://dx.doi.org/10.1097/MD.0000000000034068
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author Kao, Hsu-Ju
Chien, Tsair-Wei
Wang, Wen-Chung
Chou, Willy
Chow, Julie Chi
author_facet Kao, Hsu-Ju
Chien, Tsair-Wei
Wang, Wen-Chung
Chou, Willy
Chow, Julie Chi
author_sort Kao, Hsu-Ju
collection PubMed
description The application of large language models in clinical decision support (CDS) is an area that warrants further investigation. ChatGPT, a prominent large language models developed by OpenAI, has shown promising performance across various domains. However, there is limited research evaluating its use specifically in pediatric clinical decision-making. This study aimed to assess ChatGPT’s potential as a CDS tool in pediatrics by evCDSaluating its performance on 8 common clinical symptom prompts. Study objectives were to answer the 2 research questions: the ChatGPT’s overall grade in a range from A (high) to E (low) compared to a normal sample and the difference in assessment of ChatGPT between 2 pediatricians. METHODS: We compared ChatGPT’s responses to 8 items related to clinical symptoms commonly encountered by pediatricians. Two pediatricians independently assessed the answers provided by ChatGPT in an open-ended format. The scoring system ranged from 0 to 100, which was then transformed into 5 ordinal categories. We simulated 300 virtual students with a normal distribution to provide scores on items based on Rasch rating scale model and their difficulties in a range between −2 to 2.5 logits. Two visual presentations (Wright map and KIDMAP) were generated to answer the 2 research questions outlined in the objectives of the study. RESULTS: The 2 pediatricians’ assessments indicated that ChatGPT’s overall performance corresponded to a grade of C in a range from A to E, with average scores of −0.89 logits and 0.90 logits (=log odds), respectively. The assessments revealed a significant difference in performance between the 2 pediatricians (P < .05), with scores of −0.89 (SE = 0.37) and 0.90 (SE = 0.41) in log odds units (logits in Rasch analysis). CONCLUSION: This study demonstrates the feasibility of utilizing ChatGPT as a CDS tool for patients presenting with common pediatric symptoms. The findings suggest that ChatGPT has the potential to enhance clinical workflow and aid in responsible clinical decision-making. Further exploration and refinement of ChatGPT’s capabilities in pediatric care can potentially contribute to improved healthcare outcomes and patient management.
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spelling pubmed-102896332023-06-24 Assessing ChatGPT’s capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis Kao, Hsu-Ju Chien, Tsair-Wei Wang, Wen-Chung Chou, Willy Chow, Julie Chi Medicine (Baltimore) 6200 The application of large language models in clinical decision support (CDS) is an area that warrants further investigation. ChatGPT, a prominent large language models developed by OpenAI, has shown promising performance across various domains. However, there is limited research evaluating its use specifically in pediatric clinical decision-making. This study aimed to assess ChatGPT’s potential as a CDS tool in pediatrics by evCDSaluating its performance on 8 common clinical symptom prompts. Study objectives were to answer the 2 research questions: the ChatGPT’s overall grade in a range from A (high) to E (low) compared to a normal sample and the difference in assessment of ChatGPT between 2 pediatricians. METHODS: We compared ChatGPT’s responses to 8 items related to clinical symptoms commonly encountered by pediatricians. Two pediatricians independently assessed the answers provided by ChatGPT in an open-ended format. The scoring system ranged from 0 to 100, which was then transformed into 5 ordinal categories. We simulated 300 virtual students with a normal distribution to provide scores on items based on Rasch rating scale model and their difficulties in a range between −2 to 2.5 logits. Two visual presentations (Wright map and KIDMAP) were generated to answer the 2 research questions outlined in the objectives of the study. RESULTS: The 2 pediatricians’ assessments indicated that ChatGPT’s overall performance corresponded to a grade of C in a range from A to E, with average scores of −0.89 logits and 0.90 logits (=log odds), respectively. The assessments revealed a significant difference in performance between the 2 pediatricians (P < .05), with scores of −0.89 (SE = 0.37) and 0.90 (SE = 0.41) in log odds units (logits in Rasch analysis). CONCLUSION: This study demonstrates the feasibility of utilizing ChatGPT as a CDS tool for patients presenting with common pediatric symptoms. The findings suggest that ChatGPT has the potential to enhance clinical workflow and aid in responsible clinical decision-making. Further exploration and refinement of ChatGPT’s capabilities in pediatric care can potentially contribute to improved healthcare outcomes and patient management. Lippincott Williams & Wilkins 2023-06-23 /pmc/articles/PMC10289633/ /pubmed/37352054 http://dx.doi.org/10.1097/MD.0000000000034068 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.
spellingShingle 6200
Kao, Hsu-Ju
Chien, Tsair-Wei
Wang, Wen-Chung
Chou, Willy
Chow, Julie Chi
Assessing ChatGPT’s capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis
title Assessing ChatGPT’s capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis
title_full Assessing ChatGPT’s capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis
title_fullStr Assessing ChatGPT’s capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis
title_full_unstemmed Assessing ChatGPT’s capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis
title_short Assessing ChatGPT’s capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis
title_sort assessing chatgpt’s capacity for clinical decision support in pediatrics: a comparative study with pediatricians using kidmap of rasch analysis
topic 6200
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289633/
https://www.ncbi.nlm.nih.gov/pubmed/37352054
http://dx.doi.org/10.1097/MD.0000000000034068
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