Evidence map›Paper›PMID 38895543›Full record

ArticleSAGE open medicine2024

ChatGPT-3.5 passes Poland's medical final examination-Is it possible for ChatGPT to become a doctor in Poland?

Szymon Suwała, Paulina Szulc, Cezary Guzowski, Barbara Kamińska, Jakub Dorobiała, Karolina Wojciechowska, Maria Berska, Olga Kubicka, Oliwia Kosturkiewicz, Bernadetta Kosztulska and 2 more

Abstract read
In one paragraph

Article in SAGE open medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Article
  3. Review
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  5. 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

12 authors.

Szymon SuwałaDepartment of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.ORCID https://orcid.org/0000-0002-5865-8484
Paulina SzulcEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Cezary GuzowskiEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Barbara KamińskaEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Jakub DorobiałaEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Karolina WojciechowskaEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Maria BerskaEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Olga KubickaEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Oliwia KosturkiewiczEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Bernadetta KosztulskaEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Alicja RajewskaEvidence-Based Medicine Students Scientific Club of Department of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Roman JunikDepartment of Endocrinology and Diabetology, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: ChatGPT is an advanced chatbot based on Large Language Model that has the ability to answer questions. Undoubtedly, ChatGPT is capable of transforming communication, education, and customer support; however, can it play the role of a doctor? In Poland, prior to obtaining a medical diploma, candidates must successfully pass the Medical Final Examination. Methods: The purpose of this research was to determine how well ChatGPT performed on the Polish Medical Final Examination, which passing is required to become a doctor in Poland (an exam is considered passed if at least 56% of the tasks are answered correctly). A total of 2138 categorized Medical Final Examination questions (from 11 examination sessions held between 2013-2015 and 2021-2023) were presented to ChatGPT-3.5 from 19 to 26 May 2023. For further analysis, the questions were divided into quintiles based on difficulty and duration, as well as question types (simple A-type or complex K-type). The answers provided by ChatGPT were compared to the official answer key, reviewed for any changes resulting from the advancement of medical knowledge. Results: ChatGPT correctly answered 53.4%-64.9% of questions. In 8 out of 11 exam sessions, ChatGPT achieved the scores required to successfully pass the examination (60%). The correlation between the efficacy of artificial intelligence and the level of complexity, difficulty, and length of a question was found to be negative. AI outperformed humans in one category: psychiatry (77.18% vs. 70.25%, Conclusions: The performance of artificial intelligence is deemed satisfactory; however, it is observed to be markedly inferior to that of human graduates in the majority of instances. Despite its potential utility in many medical areas, ChatGPT is constrained by its inherent limitations that prevent it from entirely supplanting human expertise and knowledge.

Indexed as

ChatGPTeducationmedicinepublic health

Identifiers

PMID38895543
PMCPMC11185017

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.