Evidence map›Paper›PMID 41141004›Full record

ArticleCureus2025

Comparison of GPT-5 Responses With the Official Results of the Polish Specialized Psychiatric Examination in Child and Adolescent Psychiatry.

Anna Kowalczyk, Michalina Loson-Kawalec, Aleksander Tabor, Patrycja Dadynska, Katarzyna Romanowicz, Aleksandra Wielochowska, Dawid Boczkowski, Weronika Majchrowicz, Tomasz Dolata, Piotr Sawina and 5 more

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

15 authors.

Anna KowalczykFaculty of Medicine, University of Opole, Opole, POL.
Michalina Loson-KawalecFaculty of Medicine, University of Opole, Opole, POL.
Aleksander TaborFaculty of Medicine, University of Opole, Opole, POL.
Patrycja DadynskaDepartment of Internal Medicine, Karol Marcinkowski University Hospital, Zielona Góra, POL.
Katarzyna RomanowiczFaculty of Medicine, University of Opole, Opole, POL.
Aleksandra WielochowskaDepartment of Internal Medicine, Central Teaching Hospital of the Medical University of Lodz, Lodz, POL.
Dawid BoczkowskiDepartment of Internal Medicine, Central Teaching Hospital of the Medical University of Lodz, Lodz, POL.
Weronika MajchrowiczDepartment of Internal Medicine, Multispecialty Independent Public Health Care Institution Hospital in Nowa Sól, Nowa Sól, POL.
Tomasz DolataDepartment of Internal Medicine, Multispecialty Independent Public Health Care Institution Hospital in Nowa Sól, Nowa Sól, POL.
Piotr SawinaDepartment of Internal Medicine, Non-public Health Care Institution (NZOZ) Hospital in Dzierżoniów, Dzierżoniów, POL.
Dawid BartosikFaculty of Medicine, University of Opole, Opole, POL.
Alicja SzalachFaculty of Medicine, University of Opole, Opole, POL.
Marta ZerekFaculty of Medicine, University of Opole, Opole, POL.
Gracjan SitarekFaculty of Medicine, University of Opole, Opole, POL.
Dominika RadejDepartment of General and Vascular Surgery, Specialist Medical Center, Polanica-Zdrój, POL.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Artificial intelligence (AI), particularly language models such as ChatGPT (OpenAI, San Francisco, CA, USA), is becoming increasingly important in medical education and knowledge assessment. Prior studies have demonstrated the growing effectiveness of AI in preparing students for medical examinations, including the Medical Final Examination (Lekarski Egzamin Końcowy (LEK)) of Poland and the National Specialty Examination across various disciplines. This raises important questions regarding its potential role as a tool to support specialist training. Objective The aim of this study is to evaluate the effectiveness of the advanced GPT-5 model in addressing problems in child and adolescent psychiatry. The focus is on the accuracy of answers, their correctness, and the model's self-declared confidence levels to assess its potential efficacy in education. Methodology The study analyzed the official spring 2025 National Specialty Examination (Państwowy Egzamin Specjalizacyjny (PES)) of Poland in child and adolescent psychiatry. The exam consisted of 120 multiple-choice questions with a single correct answer. GPT-5 was familiarized with the examination rules and then presented with the questions in the Polish language. Answers were evaluated using the official Centre for Medical Examination (CEM) key. In addition, the model provided a confidence rating for each answer on a five-point scale. Questions were categorized as either clinical or theoretical. Statistical analysis was conducted using the chi-square test and the Mann-Whitney U test. Results GPT-5 answered 97 questions correctly (80.8%), surpassing the required passing threshold. No significant difference was observed between the accuracy of responses to clinical versus theoretical questions (p = 0.399). However, correct answers were significantly more likely when the model reported higher confidence levels (p = 0.012). Conclusions GPT-5 demonstrated strong performance in the National Specialty Examination of Poland in child and adolescent psychiatry, supporting its potential as a supplementary tool in specialist education. Confidence ratings may provide an additional metric for evaluating the reliability of answers. Nevertheless, broader integration of AI in medical education requires experts overseeing the process and further research across diverse medical disciplines.

Indexed as

artificial intelligence in educationchat gpt-5child and adolescent psychiatrymedical education technologypes

Identifiers

PMID41141004
PMCPMC12548555

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.