• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Parents' understanding and attitudes toward the application of AI in pediatric healthcare: a cross-sectional survey study.家长对人工智能在儿科医疗保健中应用的理解和态度:一项横断面调查研究。
Front Public Health. 2025 Aug 20;13:1654482. doi: 10.3389/fpubh.2025.1654482. eCollection 2025.
2
Prescription of Controlled Substances: Benefits and Risks管制药品的处方:益处与风险
3
Attitudes and readiness to adopt artificial intelligence among healthcare practitioners in Pakistan's resource-limited settings.巴基斯坦资源有限环境下医疗从业者对采用人工智能的态度及准备情况。
BMC Health Serv Res. 2025 Aug 6;25(1):1031. doi: 10.1186/s12913-025-13207-5.
4
AI in Medical Questionnaires: Innovations, Diagnosis, and Implications.医学问卷中的人工智能:创新、诊断及影响
J Med Internet Res. 2025 Jun 23;27:e72398. doi: 10.2196/72398.
5
User Intent to Use DeepSeek for Health Care Purposes and Their Trust in the Large Language Model: Multinational Survey Study.用户将DeepSeek用于医疗保健目的的意图及其对大语言模型的信任:跨国调查研究
JMIR Hum Factors. 2025 May 26;12:e72867. doi: 10.2196/72867.
6
Sexual Harassment and Prevention Training性骚扰与预防培训
7
Parents' and informal caregivers' views and experiences of communication about routine childhood vaccination: a synthesis of qualitative evidence.父母及非正式照料者关于儿童常规疫苗接种沟通的观点与经历:定性证据综述
Cochrane Database Syst Rev. 2017 Feb 7;2(2):CD011787. doi: 10.1002/14651858.CD011787.pub2.
8
The risks, benefits, and resource implications of different diets in gastrostomy-fed children: The YourTube mixed method study.胃造口喂养儿童不同饮食的风险、益处及资源影响:YouTube混合方法研究
Health Technol Assess. 2025 Jul;29(25):1-21. doi: 10.3310/RRREF7741.
9
Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients.医院患者对医疗保健和诊断领域人工智能的跨国态度。
JAMA Netw Open. 2025 Jun 2;8(6):e2514452. doi: 10.1001/jamanetworkopen.2025.14452.
10
Face-to-face interventions for informing or educating parents about early childhood vaccination.针对向父母宣传或教育幼儿疫苗接种情况的面对面干预措施。
Cochrane Database Syst Rev. 2018 May 8;5(5):CD010038. doi: 10.1002/14651858.CD010038.pub3.

本文引用的文献

1
Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions.儿科医疗保健中的机器学习:当前趋势、挑战及未来方向。
J Clin Med. 2025 Jan 26;14(3):807. doi: 10.3390/jcm14030807.
2
Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.用于小儿克罗恩病复发预测的时间汇总机器学习模型的开发
Clin Transl Gastroenterol. 2025 Jan 1;16(1):e00794. doi: 10.14309/ctg.0000000000000794.
3
Prediction of Survival After Pediatric Cardiac Arrest Using Quantitative EEG and Machine Learning Techniques.使用定量脑电图和机器学习技术预测小儿心搏骤停后的生存情况。
Neurology. 2024 Dec 24;103(12):e210043. doi: 10.1212/WNL.0000000000210043. Epub 2024 Nov 20.
4
Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities.人工智能与医疗保健:一段贯穿历史、当前创新及未来可能性的历程。
Life (Basel). 2024 Apr 26;14(5):557. doi: 10.3390/life14050557.
5
Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration.利用生成式人工智能和大语言模型:医疗保健整合综合路线图。
Healthcare (Basel). 2023 Oct 20;11(20):2776. doi: 10.3390/healthcare11202776.
6
Doctor-patient interactions in the age of AI: navigating innovation and expertise.人工智能时代的医患互动:驾驭创新与专业知识
Front Med (Lausanne). 2023 Aug 30;10:1241508. doi: 10.3389/fmed.2023.1241508. eCollection 2023.
7
A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates.基于改良 Aquila 的优化 XGBoost 框架,用于检测新生儿中可能的癫痫发作状态。
Sensors (Basel). 2023 Aug 9;23(16):7037. doi: 10.3390/s23167037.
8
Identification of Pediatric Bacterial Gastroenteritis From Blood Counts and Interviews Based on Machine Learning.基于机器学习通过血细胞计数和访谈识别儿童细菌性肠胃炎
Cureus. 2023 Aug 17;15(8):e43644. doi: 10.7759/cureus.43644. eCollection 2023 Aug.
9
Artificial Intelligence and Machine Learning in Clinical Medicine.临床医学中的人工智能与机器学习
N Engl J Med. 2023 Jun 22;388(25):2398. doi: 10.1056/NEJMc2305287.
10
Patient views on the implementation of artificial intelligence in radiotherapy.患者对放疗中人工智能应用的看法。
Radiography (Lond). 2023 May;29 Suppl 1:S112-S116. doi: 10.1016/j.radi.2023.03.006. Epub 2023 Mar 23.

家长对人工智能在儿科医疗保健中应用的理解和态度:一项横断面调查研究。

Parents' understanding and attitudes toward the application of AI in pediatric healthcare: a cross-sectional survey study.

作者信息

Huang Yan-Dan, Zeng Shu-Long, Lin Jie, Mao Zhi-Peng, Zhang Qi-Liang, Feng Gao-Zhong, Ou Qiong-Xia

机构信息

Department of Pediatrics, Yongtai County Hospital, Fuzhou, China.

Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.

出版信息

Front Public Health. 2025 Aug 20;13:1654482. doi: 10.3389/fpubh.2025.1654482. eCollection 2025.

DOI:10.3389/fpubh.2025.1654482
PMID:40910043
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12405254/
Abstract

OBJECTIVE

To investigate parents' understanding and attitudes toward the application of Artificial Intelligence (AI) in pediatric healthcare.

METHODS

An observational, cross-sectional study was conducted using an questionnaire. Between February and April 2025, 200 family members of children receiving care at our hospital voluntarily participated in the study. Inclusion criteria included being a family member of a child treated at the hospital. Exclusion criteria were an inability to understand the questionnaire and incomplete responses. The AI applications referenced by respondents primarily included large language models such as ChatGPT, DeepSeek, and Kimi, among others. The questionnaire consisted of two sections: demographic information, and attitudes toward the use of AI in pediatric healthcare. Data were analyzed using SPSS (version 25.0). Quantitative variables were expressed as mean and standard deviation, and categorical variables using frequencies and percentages. Group comparisons were performed using chi-square test and t-test (-value < 0.05).

RESULTS

A total of 185 participants completed the questionnaire. Participants who were unaware of AI applications in pediatric healthcare were more likely to be older, have lower educational levels, and reside in rural areas. The majority of respondents (71.2%) believed that the information provided by AI was partially accurate, while 6.9% considered it partially inaccurate. Regarding perceived benefits, 74% identified convenience as the main advantage of AI in pediatric care, followed by 41.1% who cited high diagnostic efficiency. Key concerns included perceived inaccuracy and the potential for misdiagnosis (52%), as well as uncertainty regarding accountability in the event of an error (44.5%). Most participants (91.1%) believed that AI cannot replace doctors in the future.

CONCLUSION

Although most parents were aware of the use of AI in pediatric healthcare and recognized its convenience and efficiency, they expressed concerns about accuracy, accountability, and data privacy. A notable lack of awareness was observed among older individuals, those with lower levels of education, and residents of rural areas.

摘要

目的

调查家长对人工智能(AI)在儿科医疗保健中应用的理解和态度。

方法

采用问卷调查进行一项观察性横断面研究。2025年2月至4月期间,在我院接受治疗的200名儿童的家庭成员自愿参与了该研究。纳入标准包括是在我院接受治疗儿童的家庭成员。排除标准为无法理解问卷和回答不完整。受访者提及的人工智能应用主要包括ChatGPT、豆包和智元机器等大语言模型。问卷由两部分组成:人口统计学信息,以及对人工智能在儿科医疗保健中使用的态度。使用SPSS(25.0版)对数据进行分析。定量变量以均值和标准差表示,分类变量以频率和百分比表示。采用卡方检验和t检验进行组间比较(P值<0.05)。

结果

共有185名参与者完成了问卷。不了解人工智能在儿科医疗保健中应用的参与者更有可能年龄较大、教育水平较低且居住在农村地区。大多数受访者(71.2%)认为人工智能提供的信息部分准确,而6.9%的受访者认为部分不准确。关于感知到的好处,74%的人认为方便是人工智能在儿科护理中的主要优势,其次是41.1%的人提到诊断效率高。主要担忧包括感知到的不准确和误诊可能性(52%),以及错误发生时责任归属的不确定性(44.5%)。大多数参与者(91.1%)认为人工智能未来无法取代医生。

结论

尽管大多数家长了解人工智能在儿科医疗保健中的应用,并认识到其便利性和效率,但他们对准确性、责任归属和数据隐私表示担忧。在老年人、教育水平较低的人和农村地区居民中,明显缺乏相关认知。