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.
Authors
- Tianle Ma (ORCID: https://orcid.org/0000-0003-2309-1489)
- Zuoqin Yan (ORCID: https://orcid.org/0000-0003-3131-1998)
- Yuheng Yan
- Chuhan Shi
- Quan Li (ORCID: https://orcid.org/0000-0003-2249-0728)
- Yang Ouyang
- Xiaojuan Ma
- Chenyang Zhang (ORCID: https://orcid.org/0009-0003-1116-4895)
- Chang Jiang (ORCID: https://orcid.org/0000-0001-9726-902X)
- He Wang (ORCID: https://orcid.org/0009-0003-2550-6139)
Institutions
- University of Illinois Urbana-Champaign (US)
- Hong Kong University of Science and Technology (HK)
- Fudan University (CN)
- ShanghaiTech University (CN)
- Zhongshan Hospital (CN)
Publication Details
- 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
- 0.00