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Comparative Performance of Medical Students, ChatGPT-3.5 and ChatGPT-4.0 in Answering Questions From a Brazilian National Medical Exam: Cross-Sectional Questionnaire Study

Comparative Performance of Medical Students, ChatGPT-3.5 and ChatGPT-4.0 in Answering Questions From a Brazilian National Medical Exam: Cross-Sectional Questionnaire Study

The present article builds upon the work of Rodrigues Alessi et al [9] in 2024, who applied GPT 3.5 to the Progress Tests (PT) of 2021, 2022, and 2023, finding an average accuracy of 68.4%, surpassing that of medical students from all years [9]. Although Brazil lacks a national medical exam for resident selection or for newly graduated doctors, the PT is a national exam in which over 50,000 medical students participated in recent editions.

Mateus Rodrigues Alessi, Heitor Augusto Gomes, Gabriel Oliveira, Matheus Lopes de Castro, Fabiano Grenteski, Leticia Miyashiro, Camila do Valle, Leticia Tozzini Tavares da Silva, Cristina Okamoto

JMIR AI 2025;4:e66552

Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review

Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review

Da Silva Barros et al [15] conducted a systematic review on ML models geared toward predicting TB treatment outcomes. Distinguished from these endeavors, our current review took a broader perspective, encompassing not only DL but also ML as integral components of AI-based methods for TB detection. Siddiqui and Garg [16] examined recent studies of intelligent techniques (ML and DL) for diagnosing pulmonary TB.

Seng Hansun, Ahmadreza Argha, Ivan Bakhshayeshi, Arya Wicaksana, Hamid Alinejad-Rokny, Greg J Fox, Siaw-Teng Liaw, Branko G Celler, Guy B Marks

J Med Internet Res 2025;27:e69068