Nonacademic predictors of China medical licensing examination by Jie Sun, Yingying Xie, Ningnannan Zhang, Jingliang Cheng, Meiyun Wang, Bing Zhang, Wenzhen Zhu, Hui Zhang, Shijun Qiu, Xiaojun Xu, Yongqiang Yu, Tong Han, Zuojun Geng, Weihua Liao, Bo Gao, Wen Qin, Feng Liu, Meng Liang, Qiang Xu, Jilian Fu, Jiayuan Xu, Mengge Liu, Peng Zhang, Wei Li, Dapeng Shi, Caihong Wang, Xi-Nian Zuo, Quan Zhang, Feng Chen, Jiance Li, Zhihan Yan, Wen Shen, Yanwei Miao, Junfang Xian, Longjiang Zhang, Kai Xu, Zhaoxiang Ye, Jing Zhang, Guangbin Cui, Chunshui Yu, for the CHIMGEN Consortium

Subjects: “…National medical licensing examination…”
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A study regarding student satisfaction on medical graduate license examinations and career guiding sessions around the globe including the NEXT (National Exit Test) exam of India... by Bhanupriya Shivshankar Pande, Amit Jagannath Patil, Aashutosh Ramakant Patel, Dnyaneshwari Pande, Vaishnavi Galande, Rohit Garde

“…To assess student satisfaction after the career counselling session on medical license exams conducted across the globe. • 2. …”
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Exploring medical interns' knowledge on hypertension by Margarita Rosa Roméu Escobar, Luis Padrón Velázquez, Beatriz Sabina Roméu, Zulema Bos Rodríguez

“…A screening test was applied to 34% of the medical interns and the agenda of 2007-2008 and 2008-2009 medical licensing examinations was reviewed. <strong>Results:</strong> 73.4% of the medical interns passed the screening test. …”
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Unveiling GPT-4V's hidden challenges behind high accuracy on USMLE questions: Observational Study by Zhichao Yang, Zonghai Yao, Mahbuba Tasmin, Parth Vashisht, Won Seok Jang, Feiyun Ouyang, Beining Wang, David McManus, Dan Berlowitz, Hong Yu

“… BackgroundRecent advancements in artificial intelligence, such as GPT-3.5 Turbo (OpenAI) and GPT-4, have demonstrated significant potential by achieving good scores on text-only United States Medical Licensing Examination (USMLE) exams and effectively answering questions from physicians. …”
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Performance of ChatGPT-3.5 and ChatGPT-4 in the Taiwan National Pharmacist Licensing Examination: Comparative Evaluation Study by Ying-Mei Wang, Hung-Wei Shen, Tzeng-Ji Chen, Shu-Chiung Chiang, Ting-Guan Lin

“…GPT-3.5 has demonstrated proficiency in various examinations, particularly the United States Medical Licensing Examination. However, GPT-4 has more advanced capabilities. …”
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Factors Associated With the Accuracy of Large Language Models in Basic Medical Science Examinations: Cross-Sectional Study by Naritsaret Kaewboonlert, Jiraphon Poontananggul, Natthipong Pongsuwan, Gun Bhakdisongkhram

“…MethodsWe used questions that were closely aligned with the content and topic distribution of Thailand’s Step 1 National Medical Licensing Examination. Variables such as the difficulty index, discrimination index, and question characteristics were collected. …”
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Large Language Models in Dental Licensing Examinations: Systematic Review and Meta-Analysis by Mingxin Liu, Tsuyoshi Okuhara, Wenbo Huang, Atsushi Ogihara, Hikari Sophia Nagao, Hiroko Okada, Takahiro Kiuchi

“…As a result, their accuracy in dental exams is lower compared to medical licensing exams. Additionally, LLMs even provide more detailed explanation for incorrect answer than correct one. …”
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Trends in Israel's Medical Administration subspecialty, 1987–2022 by Yoel Angel, Hadar Goldshtein, Nevo Barel, Gil Fire, Michael Halberthal, Adi Niv-Yagoda

“…Data on year of medical licensing, city of residence, and list of additional recognized specialties along with their respective date of completion were extracted. …”
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Observations of Score Changes Between USMLE Step 1 and Step 2 Among Students of Different Demographic Groups in a Longitudinal Clinical Curriculum by Kaitlyn Novotny, Daniel Levine, Dale Netski, Edward Simanton

“…BACKGROUND The transition of the United States Medical Licensing Examination Step 1 to a pass/fail scoring system is reshaping its role in medical students’ residency placements. …”
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The Double-Edged Sword of Third-Party Resources: Examining Use and Financial Burden of Extracurricular Tools in Medical Students [version 2; peer review: 1 approved, 2 approved wit... by Dimitrios Papanagnou, Saaniya Farhan, Andres Fernandez, Meryem Guler, Drake Kienzle, Faizaan Siddique

“…Studies suggest third-party resources are used in a task-dependent manner with a supplemental role to lectures during the preclinical years and a primary role for United States Medical Licensing Examination (USMLE) preparation during clinical years. …”
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Characteristics Associated with Successful Residency Match in General Surgery by Reagan A. Collins, BA, Kaitlyn Nimmer, BA, Salma A. Sheriff, BS, Tania K. Arora, MD, Anai N. Kothari, MD, MS, Carrie Cunningham, MD, MPH, Callisia N. Clarke, MD, MS

“…Given the recent change of the United States Medical Licensing Exam Step 1 grading to pass/fail, understanding the factors that influence GSR match success is integral to identifying potential interventions to improve match rates for diverse medical students. …”
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Evaluating accuracy and reproducibility of large language model performance on critical care assessments in pharmacy education by Huibo Yang, Mengxuan Hu, Amoreena Most, W. Anthony Hawkins, Brian Murray, Susan E. Smith, Sheng Li, Andrea Sikora

“…BackgroundLarge language models (LLMs) have demonstrated impressive performance on medical licensing and diagnosis-related exams. However, comparative evaluations to optimize LLM performance and ability in the domain of comprehensive medication management (CMM) are lacking. …”
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Have gender and ethnic disparities in ophthalmology disappeared? Insights from a workforce-based study in Israel (2006–2021) by Asaf Israeli, Eytan Z. Blumenthal, Achia Nemet, Shiri Zayit-Soudry, Hadas Pizem, Eedy Mezer

“…Results During 2006–2021, 18,624 medical licenses were issued (41.7% Israeli Medical Graduates (IsrMGs), 42.2% female), average yearly increase (AYI) was 6.9%, females and IsrMGs had an average yearly decrease of 1% and 0.53%, respectively. 561 physicians began ophthalmology residency (57.5% male, 75% Jewish, 69.9% IsrMGs), reflecting a 6.2% total AYI, but 8.1% and 4.8% for female versus male residents, respectively. …”
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AI versus human-generated multiple-choice questions for medical education: a cohort study in a high-stakes examination by Alex KK Law, Jerome So, Chun Tat Lui, Yu Fai Choi, Koon Ho Cheung, Kevin Kei-ching Hung, Colin Alexander Graham

“…Objective This study aimed to evaluate the quality and psychometric properties of ChatGPT-4o-generated MCQs compared to human-created MCQs in a high-stakes medical licensing exam. Methods A prospective cohort study was conducted among medical doctors preparing for the Primary Examination on Emergency Medicine (PEEM) organised by the Hong Kong College of Emergency Medicine in August 2024. …”
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