Evidence map›Paper›PMID 40264605›Full record

ArticleCureus2025

Evaluating Chat Generative Pretrained Transformer (GPT-4o) Problem-Solving Performance in the Japan Certificate Examination for Biomedical Engineering Class 1.

Kai Ishida

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Kai IshidaFaculty of Engineering, Shonan Institute of Technology, Fujisawa, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Chat generative pretrained transformer (ChatGPT; OpenAI, San Francisco, CA) has developed rapidly and is used in various fields, including medical engineering. Japan's Certificate Examination for Biomedical Engineering class 1 (CEBM1) is responsible for the assessment of comprehensive specialized knowledge and skills centered on the maintenance and safety management of medical devices, systems, and related equipment. This study evaluated the performance of ChatGPT (GPT-4o) on CEBM1 for comparison to human-level expectations. Methods We targeted 171 questions including testing for knowledge with fundamental, applied, and problem-solving abilities from the 26th to 28th CEBM1s. We inputted the Japanese version of questions to ChatGPT (GPT-4o), and evaluated performance based on question difficulty. No prompt optimizations were used. We compared the responses provided by ChatGPT with the correct answers. Results The number of correct answers was 39 (68.4±10.5%) for questions testing fundamental knowledge, 33 (57.9±5.3%) for questions testing applied knowledge, and 38 (59.6±8.0%) for questions testing problem-solving ability. There was no statistically significant difference among the three groups. The passing criteria of 60% or higher was achieved only for the 28th examination. However, over 80% of the questions answered incorrectly were due to a lack of knowledge or incorrect knowledge. When asked questions about the background causes and specific countermeasures for problems related to medical devices, the questions were misunderstood, and in certain cases, answers were generated as hallucinations. Conclusions Currently, ChatGPT possesses a certain level of knowledge in medical engineering; however, it cannot be considered universally accurate in solving all possible problems.

Indexed as

artificial intelligencecertificate examinationchatgptlarge language modelsmedical engineering

Identifiers

PMID40264605
PMCPMC12013459

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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.