Evidence map›Paper›PMID 40802989›Full record

ArticleJMIR medical informatics2025

Assessing the Role of Large Language Models Between ChatGPT and DeepSeek in Asthma Education for Bilingual Individuals: Comparative Study.

Yaxin Liu, Fangfei Yu, Xiaofei Zhang, Xiaohan Tong, Kui Li, Weikuan Gu, Baiquan Yu

Abstract readComparative Study
In one paragraph

Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Yaxin Liu *Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, 150081, China, +86 138 3612 4743.ORCID 0009-0006-2450-6526
Fangfei Yu *Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, 150081, China, +86 138 3612 4743.ORCID 0009-0007-1584-4491
Xiaofei ZhangDepartment of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, 150081, China, +86 138 3612 4743.ORCID 0000-0001-8901-8183
Xiaohan TongDepartment of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States.ORCID 0009-0001-6779-5966
Kui LiDepartment of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States.ORCID 0000-0002-2413-6020
Weikuan GuDepartment of Orthopaedic Surgery and Biomedical Engineering, University of Tennessee Health Science Center, Memphis, TN, United States.ORCID 0000-0003-1112-8088
Baiquan YuDepartment of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, 150081, China, +86 138 3612 4743.ORCID 0009-0000-0184-895X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Asthma is a chronic inflammatory airway disease requiring long-term management. Artificial intelligence (AI)-driven tools such as large language models (LLMs) hold potential for enhancing patient education, especially for multilingual populations. However, comparative assessments of LLMs in disease-specific, bilingual health communication are limited. Objective: This study aimed to evaluate and compare the performance of two advanced LLMs-ChatGPT-4o (OpenAI) and DeepSeek-v3 (DeepSeek AI)-in providing bilingual (English and Chinese) education for patients with asthma, focusing on accuracy, completeness, clinical relevance, and language adaptability. Methods: A total of 53 asthma-related questions were collected from real patient inquiries across 8 clinical domains. Each question was posed in both English and Chinese to ChatGPT-4o and DeepSeek-v3. Responses were evaluated using a 7D clinical quality framework (eg, completeness, consensus consistency, and reasoning ability) adapted from Google Health. Three respiratory clinicians performed blinded scoring evaluations. Descriptive statistics and Wilcoxon signed-rank tests were applied to compare performance across domains and against theoretical maximums. Results: Both models demonstrated high overall quality in generating bilingual educational content. DeepSeek-v3 outperformed ChatGPT-4o in completeness and currency, particularly in treatment-related knowledge and symptom interpretation. ChatGPT-4o showed advantages in clarity and accessibility. In English responses, ChatGPT achieved perfect scores across 5 domains, but scored lower in clinical features (mean 3.78, SD 0.16; P=.02), treatment (mean 3.90, SD 0.05; P=.03), and differential diagnosis (mean 3.83, SD 0.29; P=.08). Conclusions: ChatGPT-4o and DeepSeek-v3 each offer distinct strengths for bilingual asthma education. While ChatGPT is more suitable for general health education due to its expressive clarity, DeepSeek provides more up-to-date and comprehensive clinical content. Both models can serve as effective supplementary tools for patient self-management but cannot replace professional medical advice. Future AI health care systems should enhance clinical reasoning, ensure guideline currency, and integrate human oversight to optimize safety and accuracy.

Indexed as

Artificial IntelligenceAsthmaMultilingualismPatient Education as TopicAdultFemaleGenerative Artificial IntelligenceHumansLanguageLarge Language ModelsMaleMiddle AgedasthmaChatGPTcross-linguistic studyDeepSeekpatient education

Identifiers

PMID40802989
PMCPMC12349887

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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.