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Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards

Richard HR Roberts Orcid Logo, Stephen R Ali, Hayley Hutchings Orcid Logo, Thomas D Dobbs, Iain S Whitaker, RICHARD ROBERTS

BMJ Health & Care Informatics Online, Volume: 30, Issue: 1, Start page: e100830

Swansea University Authors: Hayley Hutchings Orcid Logo, RICHARD ROBERTS

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DOI (Published version): 10.1136/bmjhci-2023-100830

Abstract

Introduction: Amid clinicians’ challenges in staying updated with medical research, artificial intelligence (AI) tools like the large language model (LLM) ChatGPT could automate appraisal of research quality, saving time and reducing bias. This study compares the proficiency of ChatGPT3 against huma...

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Published in: BMJ Health & Care Informatics Online
ISSN: 2632-1009
Published: BMJ 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64605
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This study compares the proficiency of ChatGPT3 against human evaluation in scoring abstracts to determine its potential as a tool for evidence synthesis. Methods: We compared ChatGPT’s scoring of implant dentistry abstracts with human evaluators using the Consolidated Standards of Reporting Trials for Abstracts reporting standards checklist, yielding an overall compliance score (OCS). Bland-Altman analysis assessed agreement between human and AI-generated OCS percentages. Additional error analysis included mean difference of OCS subscores, Welch’s t-test and Pearson’s correlation coefficient. Results: Bland-Altman analysis showed a mean difference of 4.92% (95% CI 0.62%, 0.37%) in OCS between human evaluation and ChatGPT. Error analysis displayed small mean differences in most domains, with the highest in ‘conclusion’ (0.764 (95% CI 0.186, 0.280)) and the lowest in ‘blinding’ (0.034 (95% CI 0.818, 0.895)). 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spelling v2 64605 2023-09-22 Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards bdf5d5f154d339dd92bb25884b7c3652 0000-0003-4155-1741 Hayley Hutchings Hayley Hutchings true false a633f3dabcf58b6e8dc523cc40ec6992 RICHARD ROBERTS RICHARD ROBERTS true false 2023-09-22 HDAT Introduction: Amid clinicians’ challenges in staying updated with medical research, artificial intelligence (AI) tools like the large language model (LLM) ChatGPT could automate appraisal of research quality, saving time and reducing bias. This study compares the proficiency of ChatGPT3 against human evaluation in scoring abstracts to determine its potential as a tool for evidence synthesis. Methods: We compared ChatGPT’s scoring of implant dentistry abstracts with human evaluators using the Consolidated Standards of Reporting Trials for Abstracts reporting standards checklist, yielding an overall compliance score (OCS). Bland-Altman analysis assessed agreement between human and AI-generated OCS percentages. Additional error analysis included mean difference of OCS subscores, Welch’s t-test and Pearson’s correlation coefficient. Results: Bland-Altman analysis showed a mean difference of 4.92% (95% CI 0.62%, 0.37%) in OCS between human evaluation and ChatGPT. Error analysis displayed small mean differences in most domains, with the highest in ‘conclusion’ (0.764 (95% CI 0.186, 0.280)) and the lowest in ‘blinding’ (0.034 (95% CI 0.818, 0.895)). The strongest correlations between were in ‘harms’ (r=0.32, p<0.001) and ‘trial registration’ (r=0.34, p=0.002), whereas the weakest were in ‘intervention’ (r=0.02, p<0.001) and ‘objective’ (r=0.06, p<0.001). Conclusion: LLMs like ChatGPT can help automate appraisal of medical literature, aiding in the identification of accurately reported research. Possible applications of ChatGPT include integration within medical databases for abstract evaluation. Current limitations include the token limit, restricting its usage to abstracts. As AI technology advances, future versions like GPT4 could offer more reliable, comprehensive evaluations, enhancing the identification of high-quality research and potentially improving patient outcomes. Journal Article BMJ Health &amp; Care Informatics Online 30 1 e100830 BMJ 2632-1009 1 10 2023 2023-10-01 10.1136/bmjhci-2023-100830 http://dx.doi.org/10.1136/bmjhci-2023-100830 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University SU College/Department paid the OA fee The research conducted herein was funded by Swansea University. SRA and TDD are funded by the Welsh Clinical Academic Training Fellowship (no award number). SRA received a Paton Masser grant from the British Association of Plastic, Reconstructive and Aesthetic Surgeons to support this work (no award number). ISW is the surgical specialty lead for Health and Care Research Wales and the chief investigator for the Scar Free Foundation & Health and Care Research Wales Programme of Reconstructive and Regenerative Surgery Research (no award number). The Scar Free Foundation is the only medical research charity focused on scarring with the mission to achieve scar-free healing within a generation. ISW is an associate editor for the Annals of Plastic Surgery, editorial board member of BMC Medicine and takes numerous other editorial board roles. 2024-01-08T14:09:41.0336742 2023-09-22T16:34:14.2135281 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Richard HR Roberts 0000-0002-9600-5943 1 Stephen R Ali 2 Hayley Hutchings 0000-0003-4155-1741 3 Thomas D Dobbs 4 Iain S Whitaker 5 RICHARD ROBERTS 6 64605__28829__e7683bd9613b410581e2e96a863b22e8.pdf 64605.VOR.pdf 2023-10-19T10:51:29.2791263 Output 1133961 application/pdf Version of Record true © Author(s) (or their employer(s)) 2023. Published by BMJ. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards
spellingShingle Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards
Hayley Hutchings
RICHARD ROBERTS
title_short Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards
title_full Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards
title_fullStr Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards
title_full_unstemmed Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards
title_sort Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards
author_id_str_mv bdf5d5f154d339dd92bb25884b7c3652
a633f3dabcf58b6e8dc523cc40ec6992
author_id_fullname_str_mv bdf5d5f154d339dd92bb25884b7c3652_***_Hayley Hutchings
a633f3dabcf58b6e8dc523cc40ec6992_***_RICHARD ROBERTS
author Hayley Hutchings
RICHARD ROBERTS
author2 Richard HR Roberts
Stephen R Ali
Hayley Hutchings
Thomas D Dobbs
Iain S Whitaker
RICHARD ROBERTS
format Journal article
container_title BMJ Health &amp; Care Informatics Online
container_volume 30
container_issue 1
container_start_page e100830
publishDate 2023
institution Swansea University
issn 2632-1009
doi_str_mv 10.1136/bmjhci-2023-100830
publisher BMJ
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
url http://dx.doi.org/10.1136/bmjhci-2023-100830
document_store_str 1
active_str 0
description Introduction: Amid clinicians’ challenges in staying updated with medical research, artificial intelligence (AI) tools like the large language model (LLM) ChatGPT could automate appraisal of research quality, saving time and reducing bias. This study compares the proficiency of ChatGPT3 against human evaluation in scoring abstracts to determine its potential as a tool for evidence synthesis. Methods: We compared ChatGPT’s scoring of implant dentistry abstracts with human evaluators using the Consolidated Standards of Reporting Trials for Abstracts reporting standards checklist, yielding an overall compliance score (OCS). Bland-Altman analysis assessed agreement between human and AI-generated OCS percentages. Additional error analysis included mean difference of OCS subscores, Welch’s t-test and Pearson’s correlation coefficient. Results: Bland-Altman analysis showed a mean difference of 4.92% (95% CI 0.62%, 0.37%) in OCS between human evaluation and ChatGPT. Error analysis displayed small mean differences in most domains, with the highest in ‘conclusion’ (0.764 (95% CI 0.186, 0.280)) and the lowest in ‘blinding’ (0.034 (95% CI 0.818, 0.895)). The strongest correlations between were in ‘harms’ (r=0.32, p<0.001) and ‘trial registration’ (r=0.34, p=0.002), whereas the weakest were in ‘intervention’ (r=0.02, p<0.001) and ‘objective’ (r=0.06, p<0.001). Conclusion: LLMs like ChatGPT can help automate appraisal of medical literature, aiding in the identification of accurately reported research. Possible applications of ChatGPT include integration within medical databases for abstract evaluation. Current limitations include the token limit, restricting its usage to abstracts. As AI technology advances, future versions like GPT4 could offer more reliable, comprehensive evaluations, enhancing the identification of high-quality research and potentially improving patient outcomes.
published_date 2023-10-01T14:09:42Z
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