Evidence map›Paper›PMID 41313805›Full record

Observational studyJMIR formative research2025

Comparison of ChatGPT and DeepSeek on a Standardized Audiologist Qualification Examination in Chinese: Observational Study.

Beier Qi, Yan Zheng, Yuanyuan Wang, Li Xu

Abstract readObservational StudyComparative Study
In one paragraph

Observational study in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Beier QiBeijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology - Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing, China, Beijing, China.ORCID 0000-0002-7172-7181
Yan ZhengBeijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology - Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing, China, Beijing, China.ORCID 0009-0004-7882-2247
Yuanyuan WangBeijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology - Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing, China, Beijing, China.ORCID 0009-0005-2588-3203
Li XuDepartment of Hearing, Speech and Language Sciences, Ohio University, Athens, OH, United States.ORCID 0000-0002-0988-7934

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenerative artificial intelligence (GenAI), exemplified by ChatGPT and DeepSeek, is rapidly advancing and reshaping human-computer interaction with its growing reasoning capabilities and broad applications across fields such as medicine and education.

objectiveThis study aimed to evaluate the performance of 2 GenAI models (ie, GPT-4-turbo and DeepSeek-R1) on a standardized audiologist qualification examination in Chinese and to explore their potential applicability in audiology education and clinical training.

methodsThe 2024 Taiwan Audiologist Qualification Examination, comprising 300 multiple-choice questions across 6 subject areas (ie, basic hearing science, behavioral audiology, electrophysiological audiology, principles and practice of hearing devices, health and rehabilitation of the auditory and balance systems, and hearing and speech communication disorders [including professional ethics]), was used to assess the performance of the 2 GenAI models. The complete answering process and reasoning paths of the models were recorded, and performance was analyzed by overall accuracy, subject-specific scores, and question-type scores. Statistical comparisons were performed at the item level using the McNemar test.

resultsChatGPT and DeepSeek achieved overall accuracies of 80.3% (241/300) and 79.3% (238/300), respectively, which are higher than the passing criterion of the Taiwan Audiologist Qualification Examination (ie, 60% correct answers). The accuracies for the 6 subject areas were 88% (44/50), 70% (35/50), 86% (43/50), 76% (38/50), 82% (41/50), and 80% (40/50) for ChatGPT and 82% (41/50), 72% (36/50), 78% (39/50), 80% (40/50), 80% (40/50), and 84% (41/50) for DeepSeek. No significant differences were found between the two models at the item level (overall P=.79), with a small effect size (accuracy difference=+1%, Cohen h=0.02, odds ratio 0.90, 95% CI 0.53-1.52) and substantial agreement (κ=0.71). ChatGPT scored highest in basic hearing science (88%), whereas DeepSeek performed the best in hearing and speech communication disorders (84%). Both models scored lowest in behavioral audiology (ChatGPT: 70% and DeepSeek: 72%). Question-type analysis revealed that both models performed well on reverse logic questions (ChatGPT: 79/95, 83%; DeepSeek: 80/95, 84%) but performed moderately on complex multiple-choice questions (ChatGPT: 9/17, 53%; DeepSeek: 11/17, 65%). However, both models performed poorly on graph-based questions (ChatGPT: 2/11, 18%; DeepSeek: 4/11, 36%).

conclusionsBoth GenAI models demonstrated strong professional knowledge and stable reasoning ability, meeting the basic requirements of clinical audiologists and suggesting their potential as supportive tools in audiology education. However, the presence of errors underscores the need for cautious use under educator supervision. Future research should explore their performance in open-ended, real-world clinical scenarios to assess practical applicability and limitations.

Indexed as

Artificial IntelligenceAudiologistsAudiologyEducational MeasurementAdultEast Asian PeopleFemaleGenerative Artificial IntelligenceHumansMaleTaiwanAIartificial intelligenceaudiologyChatGPTDeepSeekgenerative artificial intelligencemedical education

Identifiers

PMID41313805
PMCPMC12701348

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Registered trials

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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.