Evidence map›Paper›PMID 39201195›Full record

ArticleHealthcare (Basel, Switzerland)2024

Assessment Study of ChatGPT-3.5's Performance on the Final Polish Medical Examination: Accuracy in Answering 980 Questions.

Julia Siebielec, Michal Ordak, Agata Oskroba, Anna Dworakowska, Magdalena Bujalska-Zadrozny

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

5 authors.

Julia SiebielecDepartment of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland.
Michal OrdakDepartment of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland.ORCID 0000-0001-5652-4397
Agata OskrobaDepartment of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland.
Anna DworakowskaDepartment of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland.ORCID 0000-0002-2292-8307
Magdalena Bujalska-ZadroznyDepartment of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesThe use of artificial intelligence (AI) in education is dynamically growing, and models such as ChatGPT show potential in enhancing medical education. In Poland, to obtain a medical diploma, candidates must pass the Medical Final Examination, which consists of 200 questions with one correct answer per question, is administered in Polish, and assesses students' comprehensive medical knowledge and readiness for clinical practice. The aim of this study was to determine how ChatGPT-3.5 handles questions included in this exam.

methodsThis study considered 980 questions from five examination sessions of the Medical Final Examination conducted by the Medical Examination Center in the years 2022-2024. The analysis included the field of medicine, the difficulty index of the questions, and their type, namely theoretical versus case-study questions.

resultsThe average correct answer rate achieved by ChatGPT for the five examination sessions hovered around 60% and was lower (

conclusionsThis study indicates that ChatGPT-3.5 can be an effective tool for assisting in passing the final medical exam, but the results should be interpreted cautiously. It is recommended to further verify the correctness of the answers using various AI tools.

Indexed as

artificial intelligenceChatGPTmedical exam

Identifiers

PMID39201195
PMCPMC11353589

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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.