Evidence map›Paper›PMID 37831496›Full record

ArticleJMIR formative research2023

Accuracy of ChatGPT on Medical Questions in the National Medical Licensing Examination in Japan: Evaluation Study.

Yasutaka Yanagita, Daiki Yokokawa, Shun Uchida, Junsuke Tawara, Masatomi Ikusaka

Abstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 5 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

46 citing papers in PubMed, 5 syntheses or guidelines 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.

Yasutaka YanagitaDepartment of General Medicine, Chiba University Hospital, Chiba, Japan.ORCID https://orcid.org/0000-0002-9213-8247
Daiki Yokokawa *Department of General Medicine, Chiba University Hospital, Chiba, Japan.ORCID https://orcid.org/0000-0003-0944-8664
Shun UchidaDepartment of General Medicine, Chiba University Hospital, Chiba, Japan.ORCID https://orcid.org/0000-0003-3408-9763
Junsuke TawaraDepartment of Internal Medicine, Sanmu Medical Center, Chiba, Japan.ORCID https://orcid.org/0000-0002-7842-5047
Masatomi IkusakaDepartment of General Medicine, Chiba University Hospital, Chiba, Japan.ORCID https://orcid.org/0000-0002-9760-5596

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatGPT (OpenAI) has gained considerable attention because of its natural and intuitive responses. ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers, as stated by OpenAI as a limitation. However, considering that ChatGPT is an interactive AI that has been trained to reduce the output of unethical sentences, the reliability of the training data is high and the usefulness of the output content is promising. Fortunately, in March 2023, a new version of ChatGPT, GPT-4, was released, which, according to internal evaluations, was expected to increase the likelihood of producing factual responses by 40% compared with its predecessor, GPT-3.5. The usefulness of this version of ChatGPT in English is widely appreciated. It is also increasingly being evaluated as a system for obtaining medical information in languages other than English. Although it does not reach a passing score on the national medical examination in Chinese, its accuracy is expected to gradually improve. Evaluation of ChatGPT with Japanese input is limited, although there have been reports on the accuracy of ChatGPT's answers to clinical questions regarding the Japanese Society of Hypertension guidelines and on the performance of the National Nursing Examination.

objectiveThe objective of this study is to evaluate whether ChatGPT can provide accurate diagnoses and medical knowledge for Japanese input.

methodsQuestions from the National Medical Licensing Examination (NMLE) in Japan, administered by the Japanese Ministry of Health, Labour and Welfare in 2022, were used. All 400 questions were included. Exclusion criteria were figures and tables that ChatGPT could not recognize; only text questions were extracted. We instructed GPT-3.5 and GPT-4 to input the Japanese questions as they were and to output the correct answers for each question. The output of ChatGPT was verified by 2 general practice physicians. In case of discrepancies, they were checked by another physician to make a final decision. The overall performance was evaluated by calculating the percentage of correct answers output by GPT-3.5 and GPT-4.

resultsOf the 400 questions, 292 were analyzed. Questions containing charts, which are not supported by ChatGPT, were excluded. The correct response rate for GPT-4 was 81.5% (237/292), which was significantly higher than the rate for GPT-3.5, 42.8% (125/292). Moreover, GPT-4 surpassed the passing standard (>72%) for the NMLE, indicating its potential as a diagnostic and therapeutic decision aid for physicians.

conclusionsGPT-4 reached the passing standard for the NMLE in Japan, entered in Japanese, although it is limited to written questions. As the accelerated progress in the past few months has shown, the performance of the AI will improve as the large language model continues to learn more, and it may well become a decision support system for medical professionals by providing more accurate information.

Indexed as

AIartificial intelligenceChatGPTGPT-4JapaneseNational Medical Licensing ExaminationNMLE

Identifiers

PMID37831496
PMCPMC10612006

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.