ArticleEinstein (Sao Paulo, Brazil)2026
Performance of the Artificial Intelligence large language models ChatGPT 3.5, Gemini (Google Bard), ChatGPT 4.0, and Gemini 2.5 flash in surgical subspecialty questions of Brazilian medical residency exams.
Article in Einstein (Sao Paulo, Brazil), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveGiven the rapid advancement of Artificial Intelligence and its significant impact on medical education, particularly with the development of large language models, such as ChatGPT and Gemini, ChatBots have shown an increasing ability to support clinical reasoning. Regarding this, there has been a growing interest in assessing the performance of ChatBots in medical examinations. However, there are insufficient data on these tools for addressing Brazilian medical exam-related inquiries, as well as their potential as educational tools for medical students. Therefore, we aimed to evaluate the performance of Artificial Intelligence on residency entrance exams for surgical subspecialties in six different surgical residency programs.
methodsWe analyzed the performance of ChatGPT 3.5, Gemini (Google Bard), ChatGPT 4.0, and Gemini 2.5 Flash on 464 practice questions from six major institutions in São Paulo that offer surgical medical residency programs. The questions were multiple-choice, and each had a single correct answer.
resultsOverall, ChatGPT 3.5 correctly answered 257 (55.4%), Gemini (Bard) 237 (51.1%), ChatGPT4.0 360 (77.6%), and Gemini 2.5 Flash 376 (81%) out of 464 questions, showing a substantial increase in performance.
conclusionThese findings underscore the potential of advanced large language models to support medical education. Although it is unlikely that these platforms will replace the clinical decision-making skills of surgeons trained by higher education institutions, when used appropriately, they may serve as an adjunct tool for medical education.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.