How to identify patient perception of AI voice robots in the follow-up scenario? A multimodal identity perception method based on deep learning.

Journal: Journal of biomedical informatics
PMID: 39631488

Abstract

OBJECTIVES: Post-discharge follow-up stands as a critical component of post-diagnosis management, and the constraints of healthcare resources impede comprehensive manual follow-up. However, patients are less cooperative with AI follow-up calls or may even hang up once AI voice robots are perceived. To improve the effectiveness of follow-up, alternative measures should be taken when patients perceive AI voice robots. Therefore, identifying how patients perceive AI voice robots is crucial. This study aims to construct a multimodal identity perception model based on deep learning to identify how patients perceive AI voice robots.

Authors

  • Mingjie Liu
    School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. Electronic address: mingjie_liu@hust.edu.cn.
  • Kuiyou Chen
    Computer Science and Technology Department, Donghua University, Shanghai 201620, China. Electronic address: chenkuiyou95@163.com.
  • Qing Ye
    School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430000, China.
  • Hong Wu
    Department of Liver Surgery, Liver Transplantation Division, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.

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