Machine learning and human-machine trust in healthcare: A systematic survey

被引：5

作者：

Lin, Han ^{[1
]}

Han, Jiatong ^{[2
]}

Wu, Pingping ^{[1
]}

Wang, Jiangyan ^{[3
]}

Tu, Juan ^{[4
]}

Tang, Hao ^{[5
]}

Zhu, Liuning ^{[6
]}

机构：

[1] Nanjing Audit Univ, Sch Engn Audit, Jiangsu Key Lab Publ Project Audit, Nanjing, Peoples R China

[2] Nanjing Audit Univ, Sch Informat Engn, Nanjing, Peoples R China

[3] Nanjing Audit Univ, Sch Stat & Data Sci, Nanjing, Peoples R China

[4] Nanjing Univ, Sch Phys, Key Lab Modern Acoust MOE, Nanjing, Peoples R China

[5] Swiss Fed Inst Technol, Dept Informat Technol & Elect Engn, Zurich, Switzerland

[6] Nanjing Med Univ, Affiliated Hosp 1, Jiangsu Prov Hosp, Nanjing, Peoples R China

来源：

CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY | 2024年 / 9卷 / 02期

基金：

中国国家自然科学基金;

关键词：

human-machine interaction; machine learning; trust; NETWORK;

D O I：

10.1049/cit2.12268

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As human-machine interaction (HMI) in healthcare continues to evolve, the issue of trust in HMI in healthcare has been raised and explored. It is critical for the development and safety of healthcare that humans have proper trust in medical machines. Intelligent machines that have applied machine learning (ML) technologies continue to penetrate deeper into the medical environment, which also places higher demands on intelligent healthcare. In order to make machines play a role in HMI in healthcare more effectively and make human-machine cooperation more harmonious, the authors need to build good humanmachine trust (HMT) in healthcare. This article provides a systematic overview of the prominent research on ML and HMT in healthcare. In addition, this study explores and analyses ML and three important factors that influence HMT in healthcare, and then proposes a HMT model in healthcare. Finally, general trends are summarised and issues to consider addressing in future research on HMT in healthcare are identified.

引用

页码：286 / 302

页数：17

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