Artificial Intelligence: A Private Practice Perspective

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• Artificial intelligence (AI) in radiology is an emerging field with limited adoption in general practice.
• Current AI radiology tools are predominantly used in academic settings.

Purpose of the Study:

• To review the experience of an early adopter radiology practice in implementing AI.
• To identify critical steps, challenges, and benefits associated with AI integration in radiology.

Main Methods:

• Documenting the practice's journey with AI adoption, from use case identification to workflow integration.
• Detailing the process of selecting AI products, piloting algorithms, and developing in-house solutions.
• Describing strategies for managing challenges and fostering radiologist acceptance.

Main Results:

• Early adoption of AI in radiology requires careful preparation of data, systems, and personnel.
• Successful implementation involves identifying valuable use cases, vendor selection, algorithm piloting, and in-house development.
• Radiologist acceptance and workflow integration are crucial for scalable AI adoption.

Conclusions:

• AI offers transformative potential for radiology practice, but successful implementation is complex.
• A systematic approach is necessary to navigate challenges and realize the benefits of AI in radiology.
• Preparing data, systems, and radiologists is essential for scalable and effective AI integration.