Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From...

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• The increasing number of artificial intelligence (AI) solutions presents challenges for clinical radiology.
• Integrating AI into existing imaging services requires assessing performance and workflow fit.
• Successful AI deployment necessitates collaboration between clinical, technical, and financial stakeholders.

Purpose of the Study:

• To describe a framework for implementing and supporting AI applications in radiology.
• To address the complexities of integrating AI into clinical radiology workflows.
• To ensure the structured and efficient deployment of AI tools.

Main Methods:

• Describing an institutional experience and a developed framework for AI implementation.
• Outlining the process for stakeholder collaboration in AI deployment.
• Detailing the infrastructure required for post-deployment monitoring and surveillance.

Main Results:

• The presented framework facilitates the structured deployment of AI applications.
• Collaboration among diverse stakeholders is crucial for successful AI integration.
• An established infrastructure supports the safe and proper use of AI tools post-deployment.

Conclusions:

• A systematic approach and collaborative framework are essential for adopting AI in radiology.
• Effective implementation strategies enhance the utility of AI in improving imaging services.
• Post-deployment surveillance infrastructure is vital for ensuring the safe and optimal use of AI.