Does AI explainability affect physicians’ intention to use AI?-Reference-Cited by-同舟云学术

Does AI explainability affect physicians’ intention to use AI?

Published:2022-12 Issue: Volume:168 Page:104884
ISSN:1386-5056
Container-title:International Journal of Medical Informatics
language:en
Short-container-title:International Journal of Medical Informatics

Author:

Liu Chung-Feng,Chen Zhih-Cherng,Kuo Szu-Chen,Lin Tzu-Chi

Funder

Ministry of Science and Technology, Taiwan

Publisher

Elsevier BV

Subject

Health Informatics

Reference82 articles.

1. Explainable Artificial Intelligence: Understanding Visualizing and Interpreting Deep Learning Models;Samek;ITU J. ICT Discov. - Spec. Issue 1 - Impact Artif. Intell. AI Commun. Netw. Serv.,2017

2. The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center;Liu;Healthcare (Basel),2021

3. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm;Chung;Acta Orthop.,2018

4. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review;Nwanosike;Int. J. Med. Inform.,2022

5. Building Customer Trust in Mobile Commerce;Siau;Commun. ACM.,2003

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