Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial
BackgroundChildhood asthma represents a significant challenge globally, especially in underdeveloped regions. Recent advancements in Large Language Models (LLMs), such as ChatGPT, offer promising improvements in medical service quality.MethodsThis randomized controlled trial assessed the effectivene...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2025.1519751/full |
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| author | Liwen Zhang Guijun Yang Jiajun Yuan Shuhua Yuan Jing Zhang Jiande Chen Mingyu Tang Yunqin Zhang Jilei Lin Liebin Zhao Liebin Zhao Yong Yin Yong Yin Yong Yin |
| author_facet | Liwen Zhang Guijun Yang Jiajun Yuan Shuhua Yuan Jing Zhang Jiande Chen Mingyu Tang Yunqin Zhang Jilei Lin Liebin Zhao Liebin Zhao Yong Yin Yong Yin Yong Yin |
| author_sort | Liwen Zhang |
| collection | DOAJ |
| description | BackgroundChildhood asthma represents a significant challenge globally, especially in underdeveloped regions. Recent advancements in Large Language Models (LLMs), such as ChatGPT, offer promising improvements in medical service quality.MethodsThis randomized controlled trial assessed the effectiveness of ChatGPT in enhancing physicians' childhood asthma management skills. A total of 192 doctors from varied healthcare environments in China were divided into a control group, receiving traditional medical literature training, and an intervention group, trained in utilizing ChatGPT. Assessments conducted before and after training, and a 2-week follow-up, measured the training's impact.ResultsThe intervention group showed significant improvement, with scores of test questions increasing by approximately 20 out of 100 (improving to 72 ± 8 from a baseline, vs. the control group's increase to 50 ± 9). Post-training, ChatGPT's regular usage among the intervention group jumped from 6.3% to 62%, markedly above the control group's 4.3%. Moreover, physicians in the intervention group reported higher levels of familiarity, effectiveness, satisfaction, and intention for future use of ChatGPT.ConclusionChatGPT training significantly improves childhood asthma management among physicians in underdeveloped regions. This underscores the utility of LLMs like ChatGPT as effective educational tools in medical training, highlighting the need for further research into their integration and patient outcome impacts. |
| format | Article |
| id | doaj-art-e7a1496bb50e4bc39422a4cbeaa165b1 |
| institution | Kabale University |
| issn | 2296-2360 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj-art-e7a1496bb50e4bc39422a4cbeaa165b12025-08-20T03:29:22ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602025-07-011310.3389/fped.2025.15197511519751Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trialLiwen Zhang0Guijun Yang1Jiajun Yuan2Shuhua Yuan3Jing Zhang4Jiande Chen5Mingyu Tang6Yunqin Zhang7Jilei Lin8Liebin Zhao9Liebin Zhao10Yong Yin11Yong Yin12Yong Yin13Department of Respiratory Medicine, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory Medicine, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaMedical Department of Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory Medicine, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory Medicine, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory Medicine, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory Medicine, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory Medicine, Linyi Maternal and Child Healthcare Hospital, Linyi Branch of Shanghai Children’s Medical Center, Linyi City, Shandong, ChinaDepartment of Respiratory Medicine, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaPediatric AI Clinical Application and Research Center, Shanghai Children’s Medical Center, Shanghai, ChinaShanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, ChinaDepartment of Respiratory Medicine, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaPediatric AI Clinical Application and Research Center, Shanghai Children’s Medical Center, Shanghai, ChinaShanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, ChinaBackgroundChildhood asthma represents a significant challenge globally, especially in underdeveloped regions. Recent advancements in Large Language Models (LLMs), such as ChatGPT, offer promising improvements in medical service quality.MethodsThis randomized controlled trial assessed the effectiveness of ChatGPT in enhancing physicians' childhood asthma management skills. A total of 192 doctors from varied healthcare environments in China were divided into a control group, receiving traditional medical literature training, and an intervention group, trained in utilizing ChatGPT. Assessments conducted before and after training, and a 2-week follow-up, measured the training's impact.ResultsThe intervention group showed significant improvement, with scores of test questions increasing by approximately 20 out of 100 (improving to 72 ± 8 from a baseline, vs. the control group's increase to 50 ± 9). Post-training, ChatGPT's regular usage among the intervention group jumped from 6.3% to 62%, markedly above the control group's 4.3%. Moreover, physicians in the intervention group reported higher levels of familiarity, effectiveness, satisfaction, and intention for future use of ChatGPT.ConclusionChatGPT training significantly improves childhood asthma management among physicians in underdeveloped regions. This underscores the utility of LLMs like ChatGPT as effective educational tools in medical training, highlighting the need for further research into their integration and patient outcome impacts.https://www.frontiersin.org/articles/10.3389/fped.2025.1519751/fullChatGPTpediatricasthma managementlarge language modelsrandomized controlled trial |
| spellingShingle | Liwen Zhang Guijun Yang Jiajun Yuan Shuhua Yuan Jing Zhang Jiande Chen Mingyu Tang Yunqin Zhang Jilei Lin Liebin Zhao Liebin Zhao Yong Yin Yong Yin Yong Yin Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial Frontiers in Pediatrics ChatGPT pediatric asthma management large language models randomized controlled trial |
| title | Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial |
| title_full | Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial |
| title_fullStr | Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial |
| title_full_unstemmed | Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial |
| title_short | Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial |
| title_sort | enhancing pediatric asthma management in underdeveloped regions through chatgpt training for doctors a randomized controlled trial |
| topic | ChatGPT pediatric asthma management large language models randomized controlled trial |
| url | https://www.frontiersin.org/articles/10.3389/fped.2025.1519751/full |
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