[1]李红娟,杜德伟.人工智能与生育健康服务深度融合的 现实挑战及优化路径[J].卫生经济研究,2026,43(06):64-67.
 LI Hongjuan,DU Dewei.In-depth Integration of Artificial Intelligence and Reproductive Health Services: Practical Challenges and Optimization Pathways[J].Journal Press of Health Economics Research,2026,43(06):64-67.
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人工智能与生育健康服务深度融合的 现实挑战及优化路径

卫生经济研究[ISSN:1004-7778/CN:33-1056/F]

卷:
43
期数:
2026年06期
页码:
64-67
栏目:
数智健康
出版日期:
2026-06-03

文章信息/Info

Title:
In-depth Integration of Artificial Intelligence and Reproductive Health Services: Practical Challenges and Optimization Pathways
作者:
李红娟1杜德伟1
1.国家卫生健康委妇幼健康中心,北京 100191
Author(s):
LI Hongjuan DU Dewei
National Center for Women and Children's Health, National Health Commission, Beijing 100191, China
关键词:
人工智能生育健康服务算法数据安全
Keywords:
artificial intelligence reproductive health services algorithms data security
分类号:
R19
文献标志码:
A
摘要:
目的:分析人工智能与生育健康服务深度融合的应用场景及优化路径。方法:采用探索性多案例方法,分析人工智能与生育健康服务融合的典型案例。结果:人工智能与生育健康服务深度融合,常见于远程医疗、辅助诊疗、健康管理等场景,涉及9项融合业务27个融合点;在监管、数据质量与可用性、算法准确性、数据隐私与安全、临床转化、人文关怀等方面面临挑战。结论:为促进人工智能与生育健康服务的深度融合,应完善监管评估机制,激活数据要素价值,推动算法优化训练,确保数据安全可控,破解成果转化难题,最终回归生育健康服务“以人为本”的本质。
Abstract:
Objective To analyze the application scenarios and optimization pathways for the in-depth integration of artificial intelligence(AI) and reproductive health services. Methods An exploratory multiple-case study method was used to analyze typical cases of the integration of AI and reproductive health services. Results The in-depth integration of AI and reproductive health services was commonly observed in scenarios such as telemedicine, assisted diagnosis and treatment, and health management, covering 9 integrated businesses and 27 integration points. Challenges were identified in areas including regulation, data quality and availability, algorithm accuracy, data privacy and security, clinical translation, and humanistic care. Conclusion To promote the in-depth integration of AI and reproductive health services, it is necessary to improve regulatory and evaluation mechanisms, unlock the value of data elements, promote algorithm optimization and training, overcome barriers to translational research, and ultimately return to the human-centered essence of reproductive health services.

参考文献/References:

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更新日期/Last Update: 2026-06-03