MedAI-SciTS: Enhancing Interdisciplinary Collaboration between AI Researchers and Medical Experts

要旨

Integrating AI in healthcare requires effective interdisciplinary collaboration, yet challenges like methodological differences, terminology barriers, and divergent objectives persist. To address the issues, we introduce MedAI-SciTS, a structured approach combining a theoretical framework and a toolkit to improve collaboration across disciplines. The framework builds on a formative study (N=12) and literature review, identifying the key challenges and potential solutions in medical-AI projects. We further develop an innovative toolkit with twelve tools, featuring an AI-enhanced research glossary with personalized analogies, an agile co-design platform, and an integrated resource management system. A three-month case study involving AI and medical professionals (N=16 total) applying a segmentation algorithm for adrenal CT images confirmed the toolkit’s effectiveness in enhancing team engagement, communication, trust, and collaboration outcomes. We envision MedAI-SciTS could potentially be applied to a wide range of medical applications and facilitate broader medical-AI collaboration.

著者
Chen Cao
university of sheffield, Sheffield, South Yorkshire, United Kingdom
Yu Wu
Harvard University, Boston, Massachusetts, United States
Xiao Zoe. Fang
Monash University, Clayton, Victoria, Australia
Zhenwen Liang
University of Notre Dame, Notre Dame, Indiana, United States
Lena Mamykina
Columbia University, New York, New York, United States
Laura Sbaffi
University of Sheffield, Sheffield, United Kingdom
Xuhai "Orson" Xu
Columbia University, New York City, New York, United States
DOI

10.1145/3706598.3713926

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713926

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Human-Agent Interaction

Annex Hall F204
7 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
日本語まとめ
読み込み中…