Performance of three artificial intelligence (AI)‐based large language models in standardized testing; implications for AI‐assisted dental education
Introduction The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long‐term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare t...
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| Veröffentlicht in: | Journal of periodontal research Jg. 60; H. 2; S. 121 - 133 |
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| Sprache: | Englisch |
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United States
Wiley Subscription Services, Inc
01.02.2025
John Wiley and Sons Inc |
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| Abstract | Introduction
The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long‐term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare the accuracy of responses provided by ChatGPT (GPT‐4 and GPT‐3.5) and Google Gemini, the three primary large language models (LLMs), to human graduate students (control group) to the annual in‐service examination questions posed by the American Academy of Periodontology (AAP).
Methods
Under a comparative cross‐sectional study design, a corpus of 1312 questions from the annual in‐service examination of AAP administered between 2020 and 2023 were presented to the LLMs. Their responses were analyzed using chi‐square tests, and the performance was juxtaposed to the scores of periodontal residents from corresponding years, as the human control group. Additionally, two sub‐analyses were performed: one on the performance of the LLMs on each section of the exam; and in answering the most difficult questions.
Results
ChatGPT‐4 (total average: 79.57%) outperformed all human control groups as well as GPT‐3.5 and Google Gemini in all exam years (p < .001). This chatbot showed an accuracy range between 78.80% and 80.98% across the various exam years. Gemini consistently recorded superior performance with scores of 70.65% (p = .01), 73.29% (p = .02), 75.73% (p < .01), and 72.18% (p = .0008) for the exams from 2020 to 2023 compared to ChatGPT‐3.5, which achieved 62.5%, 68.24%, 69.83%, and 59.27% respectively. Google Gemini (72.86%) surpassed the average scores achieved by first‐ (63.48% ± 31.67) and second‐year residents (66.25% ± 31.61) when all exam years combined. However, it could not surpass that of third‐year residents (69.06% ± 30.45).
Conclusions
Within the confines of this analysis, ChatGPT‐4 exhibited a robust capability in answering AAP in‐service exam questions in terms of accuracy and reliability while Gemini and ChatGPT‐3.5 showed a weaker performance. These findings underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains. However, the current limitations of these models such as inability to effectively process image‐based inquiries, the propensity for generating inconsistent responses to the same prompts, and achieving high (80% by GPT‐4) but not absolute accuracy rates should be considered. An objective comparison of their capability versus their capacity is required to further develop this field of study.
ChatGPT‐4 outperforms ChatGPT‐3.5, Google Gemini, and human periodontics residents in standardized testing (AAP in‐service exams, 2020‐2023). This highlights the potential future role of AI in enhancing dental education. |
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| AbstractList | IntroductionThe emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long‐term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare the accuracy of responses provided by ChatGPT (GPT‐4 and GPT‐3.5) and Google Gemini, the three primary large language models (LLMs), to human graduate students (control group) to the annual in‐service examination questions posed by the American Academy of Periodontology (AAP).MethodsUnder a comparative cross‐sectional study design, a corpus of 1312 questions from the annual in‐service examination of AAP administered between 2020 and 2023 were presented to the LLMs. Their responses were analyzed using chi‐square tests, and the performance was juxtaposed to the scores of periodontal residents from corresponding years, as the human control group. Additionally, two sub‐analyses were performed: one on the performance of the LLMs on each section of the exam; and in answering the most difficult questions.ResultsChatGPT‐4 (total average: 79.57%) outperformed all human control groups as well as GPT‐3.5 and Google Gemini in all exam years (p < .001). This chatbot showed an accuracy range between 78.80% and 80.98% across the various exam years. Gemini consistently recorded superior performance with scores of 70.65% (p = .01), 73.29% (p = .02), 75.73% (p < .01), and 72.18% (p = .0008) for the exams from 2020 to 2023 compared to ChatGPT‐3.5, which achieved 62.5%, 68.24%, 69.83%, and 59.27% respectively. Google Gemini (72.86%) surpassed the average scores achieved by first‐ (63.48% ± 31.67) and second‐year residents (66.25% ± 31.61) when all exam years combined. However, it could not surpass that of third‐year residents (69.06% ± 30.45).ConclusionsWithin the confines of this analysis, ChatGPT‐4 exhibited a robust capability in answering AAP in‐service exam questions in terms of accuracy and reliability while Gemini and ChatGPT‐3.5 showed a weaker performance. These findings underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains. However, the current limitations of these models such as inability to effectively process image‐based inquiries, the propensity for generating inconsistent responses to the same prompts, and achieving high (80% by GPT‐4) but not absolute accuracy rates should be considered. An objective comparison of their capability versus their capacity is required to further develop this field of study. ChatGPT‐4 outperforms ChatGPT‐3.5, Google Gemini, and human periodontics residents in standardized testing (AAP in‐service exams, 2020‐2023). This highlights the potential future role of AI in enhancing dental education. The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long-term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare the accuracy of responses provided by ChatGPT (GPT-4 and GPT-3.5) and Google Gemini, the three primary large language models (LLMs), to human graduate students (control group) to the annual in-service examination questions posed by the American Academy of Periodontology (AAP).INTRODUCTIONThe emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long-term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare the accuracy of responses provided by ChatGPT (GPT-4 and GPT-3.5) and Google Gemini, the three primary large language models (LLMs), to human graduate students (control group) to the annual in-service examination questions posed by the American Academy of Periodontology (AAP).Under a comparative cross-sectional study design, a corpus of 1312 questions from the annual in-service examination of AAP administered between 2020 and 2023 were presented to the LLMs. Their responses were analyzed using chi-square tests, and the performance was juxtaposed to the scores of periodontal residents from corresponding years, as the human control group. Additionally, two sub-analyses were performed: one on the performance of the LLMs on each section of the exam; and in answering the most difficult questions.METHODSUnder a comparative cross-sectional study design, a corpus of 1312 questions from the annual in-service examination of AAP administered between 2020 and 2023 were presented to the LLMs. Their responses were analyzed using chi-square tests, and the performance was juxtaposed to the scores of periodontal residents from corresponding years, as the human control group. Additionally, two sub-analyses were performed: one on the performance of the LLMs on each section of the exam; and in answering the most difficult questions.ChatGPT-4 (total average: 79.57%) outperformed all human control groups as well as GPT-3.5 and Google Gemini in all exam years (p < .001). This chatbot showed an accuracy range between 78.80% and 80.98% across the various exam years. Gemini consistently recorded superior performance with scores of 70.65% (p = .01), 73.29% (p = .02), 75.73% (p < .01), and 72.18% (p = .0008) for the exams from 2020 to 2023 compared to ChatGPT-3.5, which achieved 62.5%, 68.24%, 69.83%, and 59.27% respectively. Google Gemini (72.86%) surpassed the average scores achieved by first- (63.48% ± 31.67) and second-year residents (66.25% ± 31.61) when all exam years combined. However, it could not surpass that of third-year residents (69.06% ± 30.45).RESULTSChatGPT-4 (total average: 79.57%) outperformed all human control groups as well as GPT-3.5 and Google Gemini in all exam years (p < .001). This chatbot showed an accuracy range between 78.80% and 80.98% across the various exam years. Gemini consistently recorded superior performance with scores of 70.65% (p = .01), 73.29% (p = .02), 75.73% (p < .01), and 72.18% (p = .0008) for the exams from 2020 to 2023 compared to ChatGPT-3.5, which achieved 62.5%, 68.24%, 69.83%, and 59.27% respectively. Google Gemini (72.86%) surpassed the average scores achieved by first- (63.48% ± 31.67) and second-year residents (66.25% ± 31.61) when all exam years combined. However, it could not surpass that of third-year residents (69.06% ± 30.45).Within the confines of this analysis, ChatGPT-4 exhibited a robust capability in answering AAP in-service exam questions in terms of accuracy and reliability while Gemini and ChatGPT-3.5 showed a weaker performance. These findings underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains. However, the current limitations of these models such as inability to effectively process image-based inquiries, the propensity for generating inconsistent responses to the same prompts, and achieving high (80% by GPT-4) but not absolute accuracy rates should be considered. An objective comparison of their capability versus their capacity is required to further develop this field of study.CONCLUSIONSWithin the confines of this analysis, ChatGPT-4 exhibited a robust capability in answering AAP in-service exam questions in terms of accuracy and reliability while Gemini and ChatGPT-3.5 showed a weaker performance. These findings underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains. However, the current limitations of these models such as inability to effectively process image-based inquiries, the propensity for generating inconsistent responses to the same prompts, and achieving high (80% by GPT-4) but not absolute accuracy rates should be considered. An objective comparison of their capability versus their capacity is required to further develop this field of study. The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long-term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare the accuracy of responses provided by ChatGPT (GPT-4 and GPT-3.5) and Google Gemini, the three primary large language models (LLMs), to human graduate students (control group) to the annual in-service examination questions posed by the American Academy of Periodontology (AAP). Under a comparative cross-sectional study design, a corpus of 1312 questions from the annual in-service examination of AAP administered between 2020 and 2023 were presented to the LLMs. Their responses were analyzed using chi-square tests, and the performance was juxtaposed to the scores of periodontal residents from corresponding years, as the human control group. Additionally, two sub-analyses were performed: one on the performance of the LLMs on each section of the exam; and in answering the most difficult questions. ChatGPT-4 (total average: 79.57%) outperformed all human control groups as well as GPT-3.5 and Google Gemini in all exam years (p < .001). This chatbot showed an accuracy range between 78.80% and 80.98% across the various exam years. Gemini consistently recorded superior performance with scores of 70.65% (p = .01), 73.29% (p = .02), 75.73% (p < .01), and 72.18% (p = .0008) for the exams from 2020 to 2023 compared to ChatGPT-3.5, which achieved 62.5%, 68.24%, 69.83%, and 59.27% respectively. Google Gemini (72.86%) surpassed the average scores achieved by first- (63.48% ± 31.67) and second-year residents (66.25% ± 31.61) when all exam years combined. However, it could not surpass that of third-year residents (69.06% ± 30.45). Within the confines of this analysis, ChatGPT-4 exhibited a robust capability in answering AAP in-service exam questions in terms of accuracy and reliability while Gemini and ChatGPT-3.5 showed a weaker performance. These findings underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains. However, the current limitations of these models such as inability to effectively process image-based inquiries, the propensity for generating inconsistent responses to the same prompts, and achieving high (80% by GPT-4) but not absolute accuracy rates should be considered. An objective comparison of their capability versus their capacity is required to further develop this field of study. Introduction The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long‐term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare the accuracy of responses provided by ChatGPT (GPT‐4 and GPT‐3.5) and Google Gemini, the three primary large language models (LLMs), to human graduate students (control group) to the annual in‐service examination questions posed by the American Academy of Periodontology (AAP). Methods Under a comparative cross‐sectional study design, a corpus of 1312 questions from the annual in‐service examination of AAP administered between 2020 and 2023 were presented to the LLMs. Their responses were analyzed using chi‐square tests, and the performance was juxtaposed to the scores of periodontal residents from corresponding years, as the human control group. Additionally, two sub‐analyses were performed: one on the performance of the LLMs on each section of the exam; and in answering the most difficult questions. Results ChatGPT‐4 (total average: 79.57%) outperformed all human control groups as well as GPT‐3.5 and Google Gemini in all exam years (p < .001). This chatbot showed an accuracy range between 78.80% and 80.98% across the various exam years. Gemini consistently recorded superior performance with scores of 70.65% (p = .01), 73.29% (p = .02), 75.73% (p < .01), and 72.18% (p = .0008) for the exams from 2020 to 2023 compared to ChatGPT‐3.5, which achieved 62.5%, 68.24%, 69.83%, and 59.27% respectively. Google Gemini (72.86%) surpassed the average scores achieved by first‐ (63.48% ± 31.67) and second‐year residents (66.25% ± 31.61) when all exam years combined. However, it could not surpass that of third‐year residents (69.06% ± 30.45). Conclusions Within the confines of this analysis, ChatGPT‐4 exhibited a robust capability in answering AAP in‐service exam questions in terms of accuracy and reliability while Gemini and ChatGPT‐3.5 showed a weaker performance. These findings underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains. However, the current limitations of these models such as inability to effectively process image‐based inquiries, the propensity for generating inconsistent responses to the same prompts, and achieving high (80% by GPT‐4) but not absolute accuracy rates should be considered. An objective comparison of their capability versus their capacity is required to further develop this field of study. ChatGPT‐4 outperforms ChatGPT‐3.5, Google Gemini, and human periodontics residents in standardized testing (AAP in‐service exams, 2020‐2023). This highlights the potential future role of AI in enhancing dental education. |
| Author | Misch, Jonathan Merchant, Keith Kumar, Purnima S. Saleh, Muhammad H. A. Hazrati, Parham Sabri, Hamoun Wang, Hom‐Lay Barootchi, Shayan |
| AuthorAffiliation | 2 Center for Clinical Research and Evidence Synthesis in Oral Tissue Regeneration (CRITERION) Ann Arbor Michigan USA 5 Division of Periodontology, Department of Oral Medicine, Infection, and Immunity Harvard School of Dental Medicine Boston Massachusetts USA 3 Naval Post‐Graduate Dental School Bethesda Maryland USA 4 Private Practice Ann Arbor Michigan USA 1 Department of Periodontics and Oral Medicine, School of Dentistry University of Michigan Ann Arbor Michigan USA |
| AuthorAffiliation_xml | – name: 2 Center for Clinical Research and Evidence Synthesis in Oral Tissue Regeneration (CRITERION) Ann Arbor Michigan USA – name: 3 Naval Post‐Graduate Dental School Bethesda Maryland USA – name: 4 Private Practice Ann Arbor Michigan USA – name: 1 Department of Periodontics and Oral Medicine, School of Dentistry University of Michigan Ann Arbor Michigan USA – name: 5 Division of Periodontology, Department of Oral Medicine, Infection, and Immunity Harvard School of Dental Medicine Boston Massachusetts USA |
| Author_xml | – sequence: 1 givenname: Hamoun orcidid: 0000-0001-6581-2104 surname: Sabri fullname: Sabri, Hamoun email: hsabri@umich.edu organization: Center for Clinical Research and Evidence Synthesis in Oral Tissue Regeneration (CRITERION) – sequence: 2 givenname: Muhammad H. A. surname: Saleh fullname: Saleh, Muhammad H. A. organization: University of Michigan – sequence: 3 givenname: Parham orcidid: 0000-0002-8362-3208 surname: Hazrati fullname: Hazrati, Parham organization: University of Michigan – sequence: 4 givenname: Keith surname: Merchant fullname: Merchant, Keith organization: Naval Post‐Graduate Dental School – sequence: 5 givenname: Jonathan surname: Misch fullname: Misch, Jonathan organization: Private Practice – sequence: 6 givenname: Purnima S. orcidid: 0000-0001-5844-1341 surname: Kumar fullname: Kumar, Purnima S. organization: University of Michigan – sequence: 7 givenname: Hom‐Lay orcidid: 0000-0003-4238-1799 surname: Wang fullname: Wang, Hom‐Lay organization: University of Michigan – sequence: 8 givenname: Shayan orcidid: 0000-0002-5347-6577 surname: Barootchi fullname: Barootchi, Shayan email: shbaroot@umich.edu organization: Harvard School of Dental Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39030766$$D View this record in MEDLINE/PubMed |
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| Snippet | Introduction
The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line... The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our... IntroductionThe emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line... ChatGPT‐4 outperforms ChatGPT‐3.5, Google Gemini, and human periodontics residents in standardized testing (AAP in‐service exams, 2020‐2023). This highlights... |
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| SubjectTerms | Accuracy American Academy of Periodontology Artificial Intelligence Chatbots ChatGPT ChatGPT‐3.5 ChatGPT‐4 Clinical Research Cross-Sectional Studies dental education Education, Dental - methods Educational Measurement - methods Educational Measurement - standards Gemini Google Bard Google Gemini Humans Large Language Models periodontal education Periodontics Periodontics - education |
| Title | Performance of three artificial intelligence (AI)‐based large language models in standardized testing; implications for AI‐assisted dental education |
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