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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Aug 14, 2024
Date Accepted: May 23, 2025

The final, peer-reviewed published version of this preprint can be found here:

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

Liu Y, Yu F, Zhang X, Tong X, Li K, Gu W, Yu B

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

JMIR Med Inform 2025;13:e65365

DOI: 10.2196/65365

PMID: 40802989

PMCID: 12349887

Assessing the Role of Large Language Models in Bilingual Asthma Patient Education: A Comparison between ChatGPT and DeepSeek

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

ABSTRACT

Background:

Asthma is a complex condition marked by persistent airway inflammation, prompting numerous investigations into the potential applications of artificial intelligence within the realm of asthma management. ChatGPT, a robust language model, has demonstrated utility across various domains such as medical education, physician consultation, medical content creation, and recommendations for lung cancer screening.

Objective:

This study seeks to evaluate the viability of ChatGPT-4.0 as a tool for supporting asthmatic patients and caregivers in the medical setting.

Methods:

A total of 33 questions regarding six aspects of asthma were gathered for analysis using ChatGPT-4.0. The responses were assessed by three respiratory specialists and categorized as "appropriate", "appropriate but insufficient", "helpful", or "inconsistent". Percentages were computed to determine the distribution of evaluations across the six aspects.

Results:

The study revealed that 60% of basic information and diagnosis were deemed "appropriate," while 40% were considered "appropriate but insufficient." In terms of clinical features, 75% were classified as "appropriate" and 25% as "helpful." Regarding treatment, 66.7% were deemed "appropriate," with 33.3% falling under the category of "appropriate but insufficient." In the assessment and management section, 81.8% were labeled as "appropriate," and 18.2% as "appropriate but insufficient." The differential diagnosis part scored 100% in appropriateness. COVID-19 related sections were rated as 75% "appropriate" and 25% "appropriate but insufficient".

Conclusions:

The results suggest that ChatGPT-4.0 could serve as a valuable supplementary tool in various aspects of asthma identification, treatment, assessment, and management beyond conventional medical practices.


 Citation

Please cite as:

Liu Y, Yu F, Zhang X, Tong X, Li K, Gu W, Yu B

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

JMIR Med Inform 2025;13:e65365

DOI: 10.2196/65365

PMID: 40802989

PMCID: 12349887

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