Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists’ Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study
BackgroundThe COVID-19 pandemic has significantly strained health care systems globally, leading to an overwhelming influx of patients and exacerbating resource limitations. Concurrently, an “infodemic” of misinformation, particularly prevalent in women’s health, has emerged....
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JMIR Publications
2025-02-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2025/1/e56126 |
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| author | Nicola Luigi Bragazzi Michèle Buchinger Hisham Atwan Ruba Tuma Francesco Chirico Lukasz Szarpak Raymond Farah Rola Khamisy-Farah |
| author_facet | Nicola Luigi Bragazzi Michèle Buchinger Hisham Atwan Ruba Tuma Francesco Chirico Lukasz Szarpak Raymond Farah Rola Khamisy-Farah |
| author_sort | Nicola Luigi Bragazzi |
| collection | DOAJ |
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BackgroundThe COVID-19 pandemic has significantly strained health care systems globally, leading to an overwhelming influx of patients and exacerbating resource limitations. Concurrently, an “infodemic” of misinformation, particularly prevalent in women’s health, has emerged. This challenge has been pivotal for health care providers, especially gynecologists and obstetricians, in managing pregnant women’s health. The pandemic heightened risks for pregnant women from COVID-19, necessitating balanced advice from specialists on vaccine safety versus known risks. In addition, the advent of generative artificial intelligence (AI), such as large language models (LLMs), offers promising support in health care. However, they necessitate rigorous testing.
ObjectiveThis study aimed to assess LLMs’ proficiency, clarity, and objectivity regarding COVID-19’s impacts on pregnancy.
MethodsThis study evaluates 4 major AI prototypes (ChatGPT-3.5, ChatGPT-4, Microsoft Copilot, and Google Bard) using zero-shot prompts in a questionnaire validated among 159 Israeli gynecologists and obstetricians. The questionnaire assesses proficiency in providing accurate information on COVID-19 in relation to pregnancy. Text-mining, sentiment analysis, and readability (Flesch-Kincaid grade level and Flesch Reading Ease Score) were also conducted.
ResultsIn terms of LLMs’ knowledge, ChatGPT-4 and Microsoft Copilot each scored 97% (32/33), Google Bard 94% (31/33), and ChatGPT-3.5 82% (27/33). ChatGPT-4 incorrectly stated an increased risk of miscarriage due to COVID-19. Google Bard and Microsoft Copilot had minor inaccuracies concerning COVID-19 transmission and complications. In the sentiment analysis, Microsoft Copilot achieved the least negative score (–4), followed by ChatGPT-4 (–6) and Google Bard (–7), while ChatGPT-3.5 obtained the most negative score (–12). Finally, concerning the readability analysis, Flesch-Kincaid Grade Level and Flesch Reading Ease Score showed that Microsoft Copilot was the most accessible at 9.9 and 49, followed by ChatGPT-4 at 12.4 and 37.1, while ChatGPT-3.5 (12.9 and 35.6) and Google Bard (12.9 and 35.8) generated particularly complex responses.
ConclusionsThe study highlights varying knowledge levels of LLMs in relation to COVID-19 and pregnancy. ChatGPT-3.5 showed the least knowledge and alignment with scientific evidence. Readability and complexity analyses suggest that each AI’s approach was tailored to specific audiences, with ChatGPT versions being more suitable for specialized readers and Microsoft Copilot for the general public. Sentiment analysis revealed notable variations in the way LLMs communicated critical information, underscoring the essential role of neutral and objective health care communication in ensuring that pregnant women, particularly vulnerable during the COVID-19 pandemic, receive accurate and reassuring guidance. Overall, ChatGPT-4, Microsoft Copilot, and Google Bard generally provided accurate, updated information on COVID-19 and vaccines in maternal and fetal health, aligning with health guidelines. The study demonstrated the potential role of AI in supplementing health care knowledge, with a need for continuous updating and verification of AI knowledge bases. The choice of AI tool should consider the target audience and required information detail level. |
| format | Article |
| id | doaj-art-19a3976f3e1b422dbabf3cea0c242b38 |
| institution | Kabale University |
| issn | 2561-326X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj-art-19a3976f3e1b422dbabf3cea0c242b382025-02-05T21:30:33ZengJMIR PublicationsJMIR Formative Research2561-326X2025-02-019e5612610.2196/56126Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists’ Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot StudyNicola Luigi Bragazzihttps://orcid.org/0000-0001-8409-868XMichèle Buchingerhttps://orcid.org/0009-0003-9186-4882Hisham Atwanhttps://orcid.org/0009-0008-0433-8152Ruba Tumahttps://orcid.org/0000-0002-1578-5854Francesco Chiricohttps://orcid.org/0000-0002-8737-4368Lukasz Szarpakhttps://orcid.org/0000-0002-0973-5455Raymond Farahhttps://orcid.org/0000-0002-9777-5106Rola Khamisy-Farahhttps://orcid.org/0000-0002-0578-7178 BackgroundThe COVID-19 pandemic has significantly strained health care systems globally, leading to an overwhelming influx of patients and exacerbating resource limitations. Concurrently, an “infodemic” of misinformation, particularly prevalent in women’s health, has emerged. This challenge has been pivotal for health care providers, especially gynecologists and obstetricians, in managing pregnant women’s health. The pandemic heightened risks for pregnant women from COVID-19, necessitating balanced advice from specialists on vaccine safety versus known risks. In addition, the advent of generative artificial intelligence (AI), such as large language models (LLMs), offers promising support in health care. However, they necessitate rigorous testing. ObjectiveThis study aimed to assess LLMs’ proficiency, clarity, and objectivity regarding COVID-19’s impacts on pregnancy. MethodsThis study evaluates 4 major AI prototypes (ChatGPT-3.5, ChatGPT-4, Microsoft Copilot, and Google Bard) using zero-shot prompts in a questionnaire validated among 159 Israeli gynecologists and obstetricians. The questionnaire assesses proficiency in providing accurate information on COVID-19 in relation to pregnancy. Text-mining, sentiment analysis, and readability (Flesch-Kincaid grade level and Flesch Reading Ease Score) were also conducted. ResultsIn terms of LLMs’ knowledge, ChatGPT-4 and Microsoft Copilot each scored 97% (32/33), Google Bard 94% (31/33), and ChatGPT-3.5 82% (27/33). ChatGPT-4 incorrectly stated an increased risk of miscarriage due to COVID-19. Google Bard and Microsoft Copilot had minor inaccuracies concerning COVID-19 transmission and complications. In the sentiment analysis, Microsoft Copilot achieved the least negative score (–4), followed by ChatGPT-4 (–6) and Google Bard (–7), while ChatGPT-3.5 obtained the most negative score (–12). Finally, concerning the readability analysis, Flesch-Kincaid Grade Level and Flesch Reading Ease Score showed that Microsoft Copilot was the most accessible at 9.9 and 49, followed by ChatGPT-4 at 12.4 and 37.1, while ChatGPT-3.5 (12.9 and 35.6) and Google Bard (12.9 and 35.8) generated particularly complex responses. ConclusionsThe study highlights varying knowledge levels of LLMs in relation to COVID-19 and pregnancy. ChatGPT-3.5 showed the least knowledge and alignment with scientific evidence. Readability and complexity analyses suggest that each AI’s approach was tailored to specific audiences, with ChatGPT versions being more suitable for specialized readers and Microsoft Copilot for the general public. Sentiment analysis revealed notable variations in the way LLMs communicated critical information, underscoring the essential role of neutral and objective health care communication in ensuring that pregnant women, particularly vulnerable during the COVID-19 pandemic, receive accurate and reassuring guidance. Overall, ChatGPT-4, Microsoft Copilot, and Google Bard generally provided accurate, updated information on COVID-19 and vaccines in maternal and fetal health, aligning with health guidelines. The study demonstrated the potential role of AI in supplementing health care knowledge, with a need for continuous updating and verification of AI knowledge bases. The choice of AI tool should consider the target audience and required information detail level.https://formative.jmir.org/2025/1/e56126 |
| spellingShingle | Nicola Luigi Bragazzi Michèle Buchinger Hisham Atwan Ruba Tuma Francesco Chirico Lukasz Szarpak Raymond Farah Rola Khamisy-Farah Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists’ Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study JMIR Formative Research |
| title | Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists’ Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study |
| title_full | Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists’ Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study |
| title_fullStr | Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists’ Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study |
| title_full_unstemmed | Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists’ Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study |
| title_short | Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists’ Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study |
| title_sort | proficiency clarity and objectivity of large language models versus specialists knowledge on covid 19 s impacts in pregnancy cross sectional pilot study |
| url | https://formative.jmir.org/2025/1/e56126 |
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