Performance of ChatGPT on the MCAT: The Road to Personalized and Equitable Premedical Learning Article Swipe
Vikas Bommineni
,
Sanaea Bhagwagar
,
Daniel Balcarcel
,
Christos Davatzikos
,
Donald Boyer
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.05.23286533
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.05.23286533
Despite an increasingly diverse population, an unmet demand for undergraduates from underrepresented racial and ethnic minority (URM) backgrounds exists in the field of medicine as a result of financial hurdles and insufficient educational support faced by URM students in the premedical journey. With the capacity to provide highly individualized and accessible no- or low-cost dynamic instruction, large language models (LLMs) and their chatbot derivatives are posed to change this dynamic and subsequently help shape a more diverse future physician workforce. While studies have established the passing performance and insightful explanations of one of the most accurate LLM-powered chatbots to date—Chat Generative Pre-trained Transformer (ChatGPT)—on standardized exams such as medical licensing exams, the role of ChatGPT in premedical education remains unknown. We evaluated the performance of ChatGPT on the Medical College Admission Test (MCAT), a standardized 230-question multiple choice exam that assesses a broad range of competencies in the natural, physical, social, and behavioral sciences as well as critical analysis and reasoning. Depending on its visual item response strategy, ChatGPT performed at or above the median performance of 276,779 student test takers on the MCAT. Additionally, ChatGPT-generated answers demonstrated both a high level of agreement with the official answer key as well as insight into its explanations. Based on these promising results, we anticipate two primary applications of ChatGPT and future LLM iterations in premedical education: firstly, such models could provide free or low-cost access to personalized and insightful explanations of MCAT competency-related questions to help students from all socioeconomic and URM backgrounds. Secondly, these models could be used to generate additional test questions by test-makers or for targeted preparation by pre-medical students. These applications of ChatGPT in premedical education could be an invaluable, innovative path forward to increase diversity and improve equity among premedical students.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.03.05.23286533
- https://www.medrxiv.org/content/medrxiv/early/2023/06/06/2023.03.05.23286533.full.pdf
- OA Status
- green
- Cited By
- 50
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323352552
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4323352552Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.03.05.23286533Digital Object Identifier
- Title
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Performance of ChatGPT on the MCAT: The Road to Personalized and Equitable Premedical LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-07Full publication date if available
- Authors
-
Vikas Bommineni, Sanaea Bhagwagar, Daniel Balcarcel, Christos Davatzikos, Donald BoyerList of authors in order
- Landing page
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https://doi.org/10.1101/2023.03.05.23286533Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2023/06/06/2023.03.05.23286533.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2023/06/06/2023.03.05.23286533.full.pdfDirect OA link when available
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Workforce, Medical education, Test (biology), Multiple choice, Population, Ethnic group, Medicine, Underrepresented Minority, Psychology, Political science, Internal medicine, Biology, Paleontology, Significant difference, Law, Environmental healthTop concepts (fields/topics) attached by OpenAlex
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50Total citation count in OpenAlex
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2025: 14, 2024: 22, 2023: 14Per-year citation counts (last 5 years)
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17Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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