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

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.