Seamless Integration of Artificial Intelligence Into the Clinical Environment: Our Experience With a Novel...

Area of Science:

• Radiology
• Artificial Intelligence
• Medical Imaging

Background:

• Growing interest in AI algorithms for clinical applications.
• Limited literature on implementing AI tools into daily clinical practice.
• Need for practical guidance on integrating AI in radiology workflows.

Purpose of the Study:

• To describe the experience of integrating an AI-enabled mobile x-ray system.
• To demonstrate an end-to-end solution for detecting pneumothoraces.
• To provide insights for other radiology enterprises on AI implementation.

Main Methods:

• Integration of an AI-enabled mobile x-ray scanner with an FDA-approved algorithm.
• Development of a workflow for extracting, delivering, and prioritizing positive studies.
• Case study analysis of the AI algorithm and PACS workflow.

Main Results:

• Successful integration of AI technology into thoracic radiology workflow.
• Demonstration of real-time detection and prioritization of pneumothorax cases.
• Identification of key insights from practical AI implementation.

Conclusions:

• AI-enabled tools can be successfully integrated into clinical radiology workflows.
• Practical implementation details are crucial for effective AI adoption.
• This experience can guide other institutions in evaluating and implementing AI solutions.