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可信病理人工智能:从理论到实践

周燕燕 邓杨 包骥 步宏

周燕燕, 邓杨, 包骥, 步宏. 可信病理人工智能:从理论到实践[J]. 协和医学杂志, 2022, 13(4): 525-529. doi: 10.12290/xhyxzz.2022-0184
引用本文: 周燕燕, 邓杨, 包骥, 步宏. 可信病理人工智能:从理论到实践[J]. 协和医学杂志, 2022, 13(4): 525-529. doi: 10.12290/xhyxzz.2022-0184
ZHOU Yanyan, DENG Yang, BAO Ji, BU Hong. Trusted Artificial Intelligence for Pathology: From Theory to Practice[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(4): 525-529. doi: 10.12290/xhyxzz.2022-0184
Citation: ZHOU Yanyan, DENG Yang, BAO Ji, BU Hong. Trusted Artificial Intelligence for Pathology: From Theory to Practice[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(4): 525-529. doi: 10.12290/xhyxzz.2022-0184

可信病理人工智能:从理论到实践

doi: 10.12290/xhyxzz.2022-0184
基金项目: 

成都市新型产业技术研究院技术创新项目 2017-CY02-00026-GX

四川大学华西医院临床研究孵化项目 20HXFH029

四川大学华西医院学科卓越发展1·3·5工程项目 ZYGD18012

详细信息
    通讯作者:

    包骥, E-mail:baoji@scu.edu.cn

  • 中图分类号: R818.02; R36

Trusted Artificial Intelligence for Pathology: From Theory to Practice

Funds: 

Technological Innovation Project of Chengdu New Industrial Technology Research Institute 2017-CY02-00026-GX

1·3·5 Project for Disciplines of Excellence Clinical Research Incubation Project, West China Hospital, Sichuan University 20HXFH029

1·3·5 Project for Disciplines of Excellence, West China Hospital ZYGD18012

More Information
  • 摘要: 人工智能正在融入病理学研究的各个领域,但在临床实践中却遇到了诸多挑战,包括因医疗数据隐私保护而形成“数据孤岛”,不利于人工智能模型的训练;现有人工智能模型缺乏可解释性,导致使用者无法理解而难以形成人机互动;人工智能模型对多模态数据利用不足,致使其预测效能难以进一步提升等。为解决上述难题,我们建议在现有病理人工智能研究中引入可信人工智能技术:(1)数据安全共享,在坚持数据保护的基础上打破数据孤岛,使用联邦学习的方法、仅调用数据训练的结果而不上传数据本身,在不影响数据安全的情况下大大增加可用于训练的数据量;(2)赋予人工智能可解释性,使用图神经网络技术模拟病理医生学习病理诊断的过程,使得模型本身具有可解释性;(3)多模态信息融合,使用知识图谱技术对更为多样和全面的数据来源进行整合并深入挖掘分析,获得更准确的模型。相信通过此三方面的工作,可信病理人工智能技术可使病理人工智能达到可控可靠和明确责任,从而促进病理人工智能的发展和临床应用。
    作者贡献:周燕燕负责查阅文献、撰写论文;邓杨负责整理文献和论文修订;包骥、步宏负责论文构思及终稿审校。
    利益冲突:所有作者均声明不存在利益冲突
  • 图  1  图神经网络预测过程

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出版历程
  • 收稿日期:  2022-04-06
  • 录用日期:  2022-05-26
  • 网络出版日期:  2022-06-10
  • 刊出日期:  2022-07-30

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