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arXiv 2408.12305 ↗First posted 22 Aug 2024cs.AIcited by 0

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Abstract (arXiv)

The potential of artificial intelligence in medical education and assessment has been made evident by recent developments in natural language processing and artificial intelligence. Medical questions can now be successfully answered by artificial intelligence algorithms. It can help medical practitioners. This study evaluates the performance of three different artificial intelligence models in answering Turkish medical questions in the 2021 1st Term Medical Specialization Examination (MSE). MSE consists of a total of 240 questions across clinical (CMST) and basic (BMST) medical sciences. According to the results in CMST, it was concluded that Gemini correctly answered 82 questions, ChatGPT-4 answered 105 questions and ChatGPT-4o answered 117 questions. In BMST, Gemini and ChatGPT-4 answered 93 questions and ChatGPT-4o answered 107 questions correctly according to the answer key. ChatGPT-4o outperformed the candidate with the highest scores of 113 and 106 according to CMST and BMST respectively. This study highlights the importance of the potential of artificial intelligence in medical education and assessment. It demonstrates that advanced models can achieve high accuracy and contextual understanding, demonstrating their potential role in medical education and evaluation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 22 Aug 2024.

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References · 5

  1. 2024Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Datacited by 65
  2. 2024Bias patterns in the application of LLMs for clinical decision support: A comprehensive studycited by 27
  3. 2023MedDM:LLM-executable clinical guidance tree for clinical decision-makingcited by 19
  4. 2024The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchiescited by 5
  5. 2023Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncologycited by 3

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