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Evaluating the Capabilities of Generative AI Tools in Understanding Medical Papers: Qualitative Study

Evaluating the Capabilities of Generative AI Tools in Understanding Medical Papers: Qualitative Study

RAG involves providing the LLMs with a prompt that instructs them to answer while staying relevant to the given document, ensuring responses align with the provided information. The results of this study will provide valuable information for medical professionals, researchers, and developers seeking to leverage the potential of LLMs for improving medical literature comprehension and ultimately enhance patient care and research efficiency.

Seyma Handan Akyon, Fatih Cagatay Akyon, Ahmet Sefa Camyar, Fatih Hızlı, Talha Sari, Şamil Hızlı

JMIR Med Inform 2024;12:e59258

Influence of Model Evolution and System Roles on ChatGPT’s Performance in Chinese Medical Licensing Exams: Comparative Study

Influence of Model Evolution and System Roles on ChatGPT’s Performance in Chinese Medical Licensing Exams: Comparative Study

The CNMLE 2022 covered 600 single-answer multiple-choice questions, which were evenly divided into 4 units [22]. Each unit had 4 specific question types: A1, the single-sentence optimal choice questions; A2, case summary optimal choice questions; A3/A4, case group optimal choice questions; and B1, standard combination questions. Detailed explanations of each question type was conveyed to Chat GPT via a structured prompt prior to inquiry (see in Multimedia Appendix 1).

Shuai Ming, Qingge Guo, Wenjun Cheng, Bo Lei

JMIR Med Educ 2024;10:e52784

Performance of GPT-4V in Answering the Japanese Otolaryngology Board Certification Examination Questions: Evaluation Study

Performance of GPT-4V in Answering the Japanese Otolaryngology Board Certification Examination Questions: Evaluation Study

We compiled the correct answer rate and the number of answered and unanswered questions, then conducted an analysis based on the presence of images, the different prompts, the content of the questions, and the associated fields. In addition, the case in which the respondent with no options, and refrained from giving a medical answer was counted as “Output errors.”

Masao Noda, Takayoshi Ueno, Ryota Koshu, Yuji Takaso, Mari Dias Shimada, Chizu Saito, Hisashi Sugimoto, Hiroaki Fushiki, Makoto Ito, Akihiro Nomura, Tomokazu Yoshizaki

JMIR Med Educ 2024;10:e57054

Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: Evaluation Study

Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: Evaluation Study

General questions are about basic medical knowledge, and one is required to choose the correct answer among options for a short question text (typically of 1 or 2 sentences) with an image. Some clinical questions consisted of multiple subquestions, in which case the background common to all the subquestions was first described, followed by the subquestions.

Takahiro Nakao, Soichiro Miki, Yuta Nakamura, Tomohiro Kikuchi, Yukihiro Nomura, Shouhei Hanaoka, Takeharu Yoshikawa, Osamu Abe

JMIR Med Educ 2024;10:e54393

Assessing ChatGPT’s Mastery of Bloom’s Taxonomy Using Psychosomatic Medicine Exam Questions: Mixed-Methods Study

Assessing ChatGPT’s Mastery of Bloom’s Taxonomy Using Psychosomatic Medicine Exam Questions: Mixed-Methods Study

For the main category, we used the correct or incorrect answer to the question, then further focused primarily on incorrect answers. In the answer texts, individual reasoning was categorized according to Bloom’s taxonomy as revised by Krathwohl [8]. Briefly, we used the following definitions of the cognitive domains for our rating procedure: Remember: retrieving relevant knowledge from long-term memory.

Anne Herrmann-Werner, Teresa Festl-Wietek, Friederike Holderried, Lea Herschbach, Jan Griewatz, Ken Masters, Stephan Zipfel, Moritz Mahling

J Med Internet Res 2024;26:e52113

A Generative Pretrained Transformer (GPT)–Powered Chatbot as a Simulated Patient to Practice History Taking: Prospective, Mixed Methods Study

A Generative Pretrained Transformer (GPT)–Powered Chatbot as a Simulated Patient to Practice History Taking: Prospective, Mixed Methods Study

These are in the form of ‘Category’: ‘Information or possible answer on request’ Chief complaint, if applicable, with: Nausea and weight loss (most recently 10 kg in 6 weeks) Chronic fatigue, exhaustion and lack of drive Localization and spread: The muscle cramps occur mainly in the legs. [ … Further details (see illness script) …] In the following, you will take the role of Ferdinand Wunderlich, […], that is, you will answer as Ferdinand Wunderlich.

Friederike Holderried, Christian Stegemann–Philipps, Lea Herschbach, Julia-Astrid Moldt, Andrew Nevins, Jan Griewatz, Martin Holderried, Anne Herrmann-Werner, Teresa Festl-Wietek, Moritz Mahling

JMIR Med Educ 2024;10:e53961

Performance Comparison of ChatGPT-4 and Japanese Medical Residents in the General Medicine In-Training Examination: Comparison Study

Performance Comparison of ChatGPT-4 and Japanese Medical Residents in the General Medicine In-Training Examination: Comparison Study

Each question was inputted once, and the answer was determined. The “correct” answers, as stipulated by JAMEP, served as the reference for comparison. Answers were deemed “correct” only if they explicitly complied with the instructions within the question text. Ambiguous responses that contained blatant errors or contained multiple choices were classified as incorrect.

Takashi Watari, Soshi Takagi, Kota Sakaguchi, Yuji Nishizaki, Taro Shimizu, Yu Yamamoto, Yasuharu Tokuda

JMIR Med Educ 2023;9:e52202