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Zuoqin Yan

dblp:383/6769 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › interactive visualization
interactive visualization system
0.812024
A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and Modeling · IEEE VIS 2024
Medical and health informatics
clinical data analysis
0.212024
A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and Modeling · IEEE VIS 2024

Methods — techniques the papers use, named apart from their topics

risk assessment · 1.5formative study · 1.5anomaly detection · 1.5
YearPublicationVenuePosition
2024 A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and Modeling
abstract
In healthcare, AI techniques are widely used for tasks like risk assessment and anomaly detection. Despite AI’s potential as a valuable assistant, its role in complex medical data analysis often over-simplifies human-AI collaboration dynamics. To address this, we collaborated with a local hospital, engaging six physicians and one data scientist in a formative study. From this collaboration, we propose a framework integrating two-phase interactive visualization systems: one for Human-Led, AI-Assisted Retrospective Analysis and another for AI-Mediated, Human-Reviewed Iterative Modeling. This framework aims to enhance understanding and discussion around effective human-AI collaboration in healthcare.
Yang Ouyang, Chenyang Zhang 0002, He Wang 0053, Tianle Ma, Chang Jiang 0001, Yuheng Yan, Zuoqin Yan, Xiaojuan Ma, Chuhan Shi, Quan Li 0002
IEEE VIS7