Evidence map›Paper›PMID 38055323›Full record

ArticleJMIR medical education2023

Performance Comparison of ChatGPT-4 and Japanese Medical Residents in the General Medicine In-Training Examination: Comparison Study.

Takashi Watari, Soshi Takagi, Kota Sakaguchi, Yuji Nishizaki, Taro Shimizu, Yu Yamamoto, Yasuharu Tokuda

Open access · goldAbstract read
In one paragraph

Article in JMIR medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 3 pooled it
2.3field-weighted citation impact, top 9% of its field
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

31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 60 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. 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

7 authors at 6 institutions in 2 countries.

Takashi WatariGeneral Medicine Center, Shimane University Hospital, Izumo, Japan.ORCID https://orcid.org/0000-0002-9322-8455
Soshi TakagiFaculty of Medicine, Shimane University, Izuom, Japan.ORCID https://orcid.org/0009-0004-3211-1626
Kota SakaguchiGeneral Medicine Center, Shimane University Hospital, Izumo, Japan.ORCID https://orcid.org/0000-0002-5169-6613
Yuji NishizakiDivision of Medical Education, Juntendo University School of Medicine, Tokyo, Japan.ORCID https://orcid.org/0000-0002-6964-6702
Taro ShimizuDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University Hospital, Tochigi, Japan.ORCID https://orcid.org/0000-0002-3788-487X
Yu YamamotoDivision of General Medicine, Center for Community Medicine, Jichi Medical University, Tochigi, Japan.ORCID https://orcid.org/0000-0003-2247-4963
Yasuharu TokudaMuribushi Okinawa Project for Teaching Hospitals, Okinawa, Japan.ORCID https://orcid.org/0000-0002-9325-7934
Dokkyo Medical University · JPJichi Medical University · JPJuntendo University · JPShimane University · JPShimane University Hospital · JPUniversity of Michigan · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe reliability of GPT-4, a state-of-the-art expansive language model specializing in clinical reasoning and medical knowledge, remains largely unverified across non-English languages.

objectiveThis study aims to compare fundamental clinical competencies between Japanese residents and GPT-4 by using the General Medicine In-Training Examination (GM-ITE).

methodsWe used the GPT-4 model provided by OpenAI and the GM-ITE examination questions for the years 2020, 2021, and 2022 to conduct a comparative analysis. This analysis focused on evaluating the performance of individuals who were concluding their second year of residency in comparison to that of GPT-4. Given the current abilities of GPT-4, our study included only single-choice exam questions, excluding those involving audio, video, or image data. The assessment included 4 categories: general theory (professionalism and medical interviewing), symptomatology and clinical reasoning, physical examinations and clinical procedures, and specific diseases. Additionally, we categorized the questions into 7 specialty fields and 3 levels of difficulty, which were determined based on residents' correct response rates.

resultsUpon examination of 137 GM-ITE questions in Japanese, GPT-4 scores were significantly higher than the mean scores of residents (residents: 55.8%, GPT-4: 70.1%; P<.001). In terms of specific disciplines, GPT-4 scored 23.5 points higher in the "specific diseases," 30.9 points higher in "obstetrics and gynecology," and 26.1 points higher in "internal medicine." In contrast, GPT-4 scores in "medical interviewing and professionalism," "general practice," and "psychiatry" were lower than those of the residents, although this discrepancy was not statistically significant. Upon analyzing scores based on question difficulty, GPT-4 scores were 17.2 points lower for easy problems (P=.007) but were 25.4 and 24.4 points higher for normal and difficult problems, respectively (P<.001). In year-on-year comparisons, GPT-4 scores were 21.7 and 21.5 points higher in the 2020 (P=.01) and 2022 (P=.003) examinations, respectively, but only 3.5 points higher in the 2021 examinations (no significant difference).

conclusionsIn the Japanese language, GPT-4 also outperformed the average medical residents in the GM-ITE test, originally designed for them. Specifically, GPT-4 demonstrated a tendency to score higher on difficult questions with low resident correct response rates and those demanding a more comprehensive understanding of diseases. However, GPT-4 scored comparatively lower on questions that residents could readily answer, such as those testing attitudes toward patients and professionalism, as well as those necessitating an understanding of context and communication. These findings highlight the strengths and limitations of artificial intelligence applications in medical education and practice.

Indexed as

answeranswersartificial intelligenceAsiaAsianchatbotchatbotsChatGPTChatGPT-4clinicalclinical trainingconversational agentconversational agentsexamexaminationexaminationsexamsGM-ITEJapanJapaneselanguage modellanguage modelsLLMmedical educationnatural language processingNLPnon-English languageperformancereasoningresidency programsresponseresponsesself-assessment

Identifiers

PMID38055323
PMCPMC10733815
OpenAlexW4389379994

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.