国际生殖健康/计划生育杂志 ›› 2026, Vol. 45 ›› Issue (5): 358-361.doi: 10.12280/gjszjk.20260221

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辅助生殖治疗患者对人工智能应用的了解及使用意愿

杨亚男, 黄云佳, 徐于睿()   

  1. 200127 上海交通大学医学院附属仁济医院生殖医学科
  • 收稿日期:2026-04-24 出版日期:2026-09-15 发布日期:2026-09-20
  • 通讯作者: 徐于睿,E-mail: xuyurui@renji.com

Research Progress on the Current Status and Willingness to Adopt Artificial Intelligence Technology among Patients Undergoing Assisted Reproductive Therapy

YANG Ya-nan, HUANG Yun-jia, XU Yu-rui()   

  1. Department of Reproductive Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
  • Received:2026-04-24 Published:2026-09-15 Online:2026-09-20
  • Contact: XU Yu-rui, E-mail: xuyurui@renji.com

摘要:

人工智能(artificial intelligence,AI)在辅助生殖领域的应用日益广泛,已涵盖女性不孕诊断、卵泡监测、胚胎选择、妊娠结局预测、男性不育评估、随访管理、患者教育及心理支持等多个环节。现有研究表明,AI有助于提高诊疗效率、优化信息管理、减轻医护工作负担,并在患者健康教育和随访质量控制中显示出一定优势。接受辅助生殖治疗的患者总体上认可AI的辅助价值,但更倾向于将其作为医生决策和护理服务的支持工具,而非替代医务人员独立决策。患者对AI的接受程度受性别、年龄、职业背景及技术亲和力等因素影响。黑箱效应、隐私与数据安全风险、情感支持不足、临床验证不充分及监管责任不明确,是影响患者使用意愿的主要顾虑因素。未来应在强化临床验证、完善伦理监管、保障数据安全和维护人文关怀的基础上,探索适合辅助生殖场景的AI应用模式,以促进其安全、规范、有效地融入临床实践并发挥实效。

关键词: 人工智能, 生殖技术, 辅助, 临床决策, 治疗, 患者意愿

Abstract:

Artificial intelligence (AI) has been applied extensively across the whole spectrum of assisted reproduction, covering female infertility diagnosis, follicular monitoring, embryo selection, pregnancy outcome prediction, male infertility assessment, follow-up management, patient education and psychological support. Existing studies have demonstrated that AI can improve clinical efficiency, streamline information administration and reduce the workload of medical staff, and that AI has certain strengths in the patient health education and follow-up quality control. Although the patients undergoing assisted reproduction treatment generally recognize the auxiliary value of AI, they prefer to regard it merely as a supporting tool for physicians' clinical judgements and nursing services, rather than a complete replacement for medical practitioners. Patients' acceptance of AI is influenced by the demographic factors including gender, age and occupational background, as well as individual technical affinity. Major concerns that hinder patients' willingness to adopt AI systems stem from the black-box effect, risks to privacy and data security, insufficient emotional support, inadequate clinical validation and ambiguous regulatory accountability. Moving forward, AI application models tailored to assisted reproductive settings should be developed on the premise of rigorous clinical verification, improved ethical supervision, guaranteed data security and retained humanistic care, so as to facilitate the safe, standardized and effective integration of AI into routine clinical practice.

Key words: Artificial intelligence, Reproductive techniques, assisted, Clinical decision-making, Therapy, Patients willingness