通过ChatGPT培训医生提升欠发达地区儿童哮喘管理：随机对照试验表1

数据集获取（Figshare）

• 源站标题：Table 1_Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial.xlsx
• DOI：10.3389/fped.2025.1519751.s001
• 发布日期：2025-07-03
• 文件数：1 个
• 文件总体积：10.25 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Enhancing_pediatric_asthma_management_in_underdeveloped_regions_through_ChatGPT_training_for_doctors_a_randomized_controlled_trial_xlsx/29467013

命令行下载

curl -L -o "Table 1_Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial.xlsx" "https://ndownloader.figshare.com/files/55947134"

文件清单

• Table 1_Enhancing pediatric asthma management in underdeveloped regions through ChatGPT training for doctors: a randomized controlled trial.xlsx（10.25 KB）

文件总体积 10.25 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

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.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fped.2025.1519751.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Enhancing_pediatric_asthma_management_in_underdeveloped_regions_through_ChatGPT_training_for_doctors_a_randomized_controlled_trial_xlsx/29467013）。