A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and Modeling

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.

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Published
2024-10-13
DOI
https://doi.org/10.1109/vis55277.2024.00028
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
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article

A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and Modeling

Tianle Ma, Zuoqin Yan, Yuheng Yan, Chuhan Shi et al.
Human-Automation Interaction and Safety
article

A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and Modeling

Tianle Ma, Zuoqin Yan, Yuheng Yan, Chuhan Shi, Quan Li, Yang Ouyang, Xiaojuan Ma, Chenyang Zhang, Chang Jiang, He Wang
article en

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.

University of Illinois Urbana-Champaign (US), Hong Kong University of Science and Technology (HK), Fudan University (CN), ShanghaiTech University (CN), Zhongshan Hospital (CN)
Openalex Percentile: Top 72%
Human-Automation Interaction and Safety
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A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and Modeling — Tianle Ma, Zuoqin Yan, et al. · (2024) | TGRS Research Map | TGRS