Cargando…
Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards
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...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583079/ https://www.ncbi.nlm.nih.gov/pubmed/37827724 http://dx.doi.org/10.1136/bmjhci-2023-100830 |
_version_ | 1785122478129938432 |
---|---|
author | Roberts, Richard HR Ali, Stephen R Hutchings, Hayley A Dobbs, Thomas D Whitaker, Iain S |
author_facet | Roberts, Richard HR Ali, Stephen R Hutchings, Hayley A Dobbs, Thomas D Whitaker, Iain S |
author_sort | Roberts, Richard HR |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-10583079 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-105830792023-10-19 Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards Roberts, Richard HR Ali, Stephen R Hutchings, Hayley A Dobbs, Thomas D Whitaker, Iain S BMJ Health Care Inform Short Report 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. BMJ Publishing Group 2023-10-12 /pmc/articles/PMC10583079/ /pubmed/37827724 http://dx.doi.org/10.1136/bmjhci-2023-100830 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Short Report Roberts, Richard HR Ali, Stephen R Hutchings, Hayley A Dobbs, Thomas D Whitaker, Iain S Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards |
title | 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_short | 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 |
topic | Short Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583079/ https://www.ncbi.nlm.nih.gov/pubmed/37827724 http://dx.doi.org/10.1136/bmjhci-2023-100830 |
work_keys_str_mv | AT robertsrichardhr comparativestudyofchatgptandhumanevaluatorsontheassessmentofmedicalliteratureaccordingtorecognisedreportingstandards AT alistephenr comparativestudyofchatgptandhumanevaluatorsontheassessmentofmedicalliteratureaccordingtorecognisedreportingstandards AT hutchingshayleya comparativestudyofchatgptandhumanevaluatorsontheassessmentofmedicalliteratureaccordingtorecognisedreportingstandards AT dobbsthomasd comparativestudyofchatgptandhumanevaluatorsontheassessmentofmedicalliteratureaccordingtorecognisedreportingstandards AT whitakeriains comparativestudyofchatgptandhumanevaluatorsontheassessmentofmedicalliteratureaccordingtorecognisedreportingstandards |