Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5 and Humans in Clinical Chemistry Multiple-Choice Questions
Advances in Medical Education and Practice, vol. Volume 15, pp. 857–871
Abstract
Introduction: Artificial intelligence (AI) chatbots excel in language understanding and generation. These models can transform healthcare education and practice. However, it is important to assess the performance of such AI models in various topics to highlight its strengths and possible limitations. This study aimed to evaluate the performance of ChatGPT (GPT-3.5 and GPT-4), Bing, and Bard compared to human students at a postgraduate master’s level in Medical Laboratory Sciences. Methods: The study design was based on the METRICS checklist for the design and reporting of AI-based studies in healthcare. The study utilized a dataset of 60 Clinical Chemistry multiple-choice questions (MCQs) initially conceived for assessing 20 MSc students. The revised Bloom’s taxonomy was used as the framework for classifying the MCQs into four cognitive categories: Remember, Understand, Analyze, and Apply. A modified version of the CLEAR tool was used for the assessment of the quality of AI-generated content, with Cohen’s κ for inter-rater agreement. Results: Compared to the mean students’ score which was 0.68± 0.23, GPT-4 scored 0.90 ± 0.30, followed by Bing (0.77 ± 0.43), GPT-3.5 (0.73 ± 0.45), and Bard (0.67 ± 0.48). Statistically significant better performance was noted in lower cognitive domains (Remember and Understand) in GPT-3.5 ( P =0.041), GPT-4 ( P =0.003), and Bard ( P =0.017) compared to the higher cognitive domains (Apply and Analyze). The CLEAR scores indicated that ChatGPT-4 performance was “Excellent” compared to the “Above average” performance of ChatGPT-3.5, Bing, and Bard. Discussion: The findings indicated that ChatGPT-4 excelled in the Clinical Chemistry exam, while ChatGPT-3.5, Bing, and Bard were above average. Given that the MCQs were directed to postgraduate students with a high degree of specialization, the performance of these AI chatbots was remarkable. Due to the risk of academic dishonesty and possible dependence on these AI models, the appropriateness of MCQs as an assessment tool in higher education should be re-evaluated. Keywords: AI in healthcare education, higher education, large language models, evaluation
Authors 5
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University of Jordan · Jordan University Hospital
Affiliation as printed
Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, Jordan
Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, Jordan
Scientific Approaches to Fight Epidemics of Infectious Diseases (SAFE-ID) Research Group, The University of Jordan, Amman, Jordan
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Affiliation as printed
Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, Jordan
Scientific Approaches to Fight Epidemics of Infectious Diseases (SAFE-ID) Research Group, The University of Jordan, Amman, Jordan
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Affiliation as printed
Scientific Approaches to Fight Epidemics of Infectious Diseases (SAFE-ID) Research Group, The University of Jordan, Amman, Jordan
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Stiftung Universitätsmedizin Essen
Affiliation as printed
Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Essen, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany
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