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Xiangmin Fan
Researcher at Chinese Academy of Sciences
Publications - 43
Citations - 584
Xiangmin Fan is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Medicine & Computer-supported cooperative work. The author has an hindex of 9, co-authored 38 publications receiving 226 citations. Previous affiliations of Xiangmin Fan include University of Pittsburgh.
Papers
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Proceedings ArticleDOI
From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People
Dakuo Wang,Elizabeth F. Churchill,Pattie Maes,Xiangmin Fan,Ben Shneiderman,Yuanchun Shi,Qianying Wang +6 more
TL;DR: This panel will bring together HCI experts who work on human collaboration and AI applications in various application contexts, from industry and academia and from both the U.S. and China.
Proceedings ArticleDOI
"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment
TL;DR: Li et al. as mentioned in this paper reported the various tensions between the design of an AI-CDSS system ("Brilliant Doctor") and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness.
Proceedings ArticleDOI
“Brilliant AI Doctor” in Rural Clinics: Challenges in AI-Powered Clinical Decision Support System Deployment
TL;DR: In this article, the authors report the various tensions between the design of an AI-CDSS system and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of the system.
Journal ArticleDOI
Utilization of Self-Diagnosis Health Chatbots in Real-World Settings: Case Study.
TL;DR: Although health chatbots are considered to be convenient tools for enhancing patient-centered care, there are issues and barriers impeding the optimal use of this novel technology.
Proceedings ArticleDOI
Understanding the Uncertainty in 1D Unidirectional Moving Target Selection
TL;DR: A Ternary-Gaussian model is found to be descriptive of the endpoint distribution in 1D unidirectional moving target selection, and two extensions of the model are demonstrated, including predicting error rates inMoving target selection; and a novel interaction technique to implicitly aid moving targets selection.