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

TL;DR

This paper introduces a two-phase visualization framework designed to improve human-AI collaboration in healthcare data analysis, emphasizing retrospective analysis and iterative modeling to foster better understanding and decision-making.

Contribution

It presents a novel two-phase interactive visualization system tailored for healthcare, addressing the gap in understanding human-AI collaboration dynamics.

Findings

Collaborated with physicians to design the framework.

Proposed systems enhance understanding of collaboration processes.

Framework supports effective decision-making in healthcare analysis.

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 oversimplifies 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.