Bridging Industry and Academia: Proceedings from the 2025 Academy Roundtable on AI Implementation in Medical Imaging

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Health Informatics

Background:

• Rapid advancements in artificial intelligence (AI) for medical imaging have not translated into widespread clinical adoption.
• Operational and structural challenges hinder the integration of AI tools into routine healthcare practices.

Purpose of the Study:

• To examine operational and structural challenges in AI development and implementation for medical imaging.
• To identify barriers across the AI lifecycle and propose mitigation strategies through multi-stakeholder collaboration.

Main Methods:

• Convened a cross-sector roundtable with researchers, department leaders, government, and industry executives in March 2025.
• Utilized a two-stage discussion framework: AI lifecycle analysis and simplified failure modes and effects analysis (sFMEA).
• Applied qualitative sFMEA to analyze process vulnerabilities and discuss mitigation approaches within identified domains.

Main Results:

• Identified key domains with implementation barriers across the AI lifecycle.
• Recurring challenges include fragmented priorities, infrastructure constraints, and regulatory complexity.
• Highlighted the need for clearer governance, consistent evaluation, and improved stakeholder coordination.

Conclusions:

• Addressing operational and structural challenges is crucial for advancing AI in medical imaging.
• Multi-stakeholder collaboration and structured analysis frameworks can identify and mitigate implementation barriers.
• Developing clear governance, defined use cases, robust implementation strategies, cost-effectiveness, and streamlined regulation are essential for successful AI integration.