e.g. mhealth
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Skip search results from other journals and go to results- 11 JMIR Medical Education
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Each candidate is presented with 600 questions, arranged in a slightly varied order, from the exam year’s question data set. According to Open AI’s introduction, Chat GPT’s responses are based on information available up to September 2021. Thus, we selected the CNMLE 2022 questions, which were purchased from a web-based bookstore [21], for our evaluation. This choice ensured that the questions had not been previously encountered and trained by the model.
JMIR Med Educ 2024;10:e52784
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Chat GPT also successfully passed the 2022 Italian Residency Admission National Exam, which consists solely of MCQs. Additionally, in the 2022 European Examination in Core Cardiology, Chat GPT answered over 60% of questions correctly, displaying consistency across various MCQs [25]. In this study, the discrepancy in Chat GPT’s performance across question formats may be attributed to the high difficulty level of these questions, even for third-year medical students.
JMIR Med Educ 2024;10:e52818
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Reference 3: Use of wearable point-of-view live streaming technology for virtual physical exam skillsexam
JMIR Med Educ 2024;10:e53193
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Development of a Novel Web-Based Tool to Enhance Clinical Skills in Medical Education
exam
JMIR Med Educ 2024;10:e47438
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In addition, as the GM-ITE is a voluntary examination, a bias toward highly motivated residents taking the exam may exist. Therefore, the generalizability of this study is not ensured. Second, causal relationships could not be guaranteed because the study design was cross-sectional. To control for selection bias and to assess causality, we believe that planning a randomized controlled trial targeting nationwide resident physicians is necessary.
JMIR Med Educ 2024;10:e52207
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However, GPT-4’s material selection is far more complex than a flat-file database with simple mapping (unless the exam questions had been in the testing data, but this is not applicable in our case). Generative tools like GPT-4 have other weaknesses and strengths. For example, they may perform relatively poorly on pure memory-recall problems but excel in topics requiring subtlety and nuanced work.
J Med Internet Res 2024;26:e57778
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Reference 14: Assessing ChatGPT's mastery of Bloom's taxonomy using psychosomatic medicine exam questionsexam
JMIR Med Educ 2024;10:e57054
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Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: Evaluation Study
exam
JMIR Med Educ 2024;10:e54393
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First, we assessed the performance of GPT-4 with a large set of psychosomatic medicine exam questions and compared the results to responses from a cohort of medical students, thereby providing human comparison and quality indicators. For a deeper understanding of the results, we used qualitative methods to comprehend the model’s performance and to assess the strengths and weaknesses of LLMs in relation to Bloom’s taxonomy.
J Med Internet Res 2024;26:e52113
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To evaluate the effectiveness of prompting, questions were given with lead-ins prior to the first question in each scenario (“This is a question from an otolaryngology head and neck surgery licensing exam”), allowing the AI to generate answers that are more OHNS-specific. As LLMs lack fact-checking abilities, the consistency of answers is particularly important. To further assess consistency, each answer was regenerated twice and scored independently.
JMIR Med Educ 2024;10:e49970
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