Evidence map›Paper›PMID 41340686›Full record

ArticleKardiochirurgia i torakochirurgia polska = Polish journal of cardio-thoracic surgery2025

ChatGPT-3.5 and the Polish thoracic surgery specialty examination: a performance evaluation.

Adam Mitręga, Dominika Kaczyńska, Mikołaj Magiera, Natalia Denisiewicz, Michał Bielówka, Anna Kożuch, Miłosz Korbaś, Aleksandra Gaweł, Jakub Kufel

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Article in Kardiochirurgia i torakochirurgia polska = Polish journal of cardio-thoracic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Adam MitręgaStudents' Scientific Association of Computer Analysis and Artificial Intelligence at the Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland.
Dominika KaczyńskaStudents' Scientific Association of Computer Analysis and Artificial Intelligence at the Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland.
Mikołaj MagieraStudents' Scientific Association of Computer Analysis and Artificial Intelligence at the Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland.
Natalia DenisiewiczStudents' Scientific Association of Computer Analysis and Artificial Intelligence at the Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland.
Michał BielówkaDepartment of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Poland.
Anna KożuchStudents' Scientific Association of Computer Analysis and Artificial Intelligence at the Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland.
Miłosz KorbaśStudents' Scientific Association of Computer Analysis and Artificial Intelligence at the Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland.
Aleksandra GawełStudents' Scientific Association of Computer Analysis and Artificial Intelligence at the Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland.
Jakub KufelDepartment of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The incredibly rapid development of artificial intelligence (AI) in recent years has created new opportunities for its application in medical advancements. This raises questions about the reliability and limitations of AI. Aim: The aim of the present study was to evaluate the effectiveness of the ChatGPT-3.5 language model in solving the test component of the National Specialist Examination (PES) in the field of thoracic surgery. Material and methods: A total of 120 test questions from 2015 PES examination were analyzed. They were grouped according to subject matter, clinical character, and cognitive requirements. In independent sessions, each question was submitted five times. The following statistical tests were applied: χ Results: The AI tool achieved a score of 42.2% correct answers, with the passing threshold set at 60%. A statistically significant difference was found between clinical and non-clinical questions ( Conclusions: The result obtained by ChatGPT-3.5 is equivalent to a failing score on the examination. The confidence of responses correlated with their correctness, whereas limitations in clinical knowledge and consistency indicate the need for caution when using this model to assess specialized knowledge.

Indexed as

artificial intelligenceChatGPTspecialist examinationthoracic surgery

Identifiers

PMID41340686
PMCPMC12670230

What OpenQuestion holds

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LicenceCC BY-NC-SA
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Registered trials

None linked

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.