Over the past five years, we’ve integrated various AI tools in the imaging lifecycle at Jefferson Einstein Hospitals, enhancing efficiency and patient care. Collaborating with AI vendors, we began with computer-aided detection (CAD) and expanded to PACS work-list integration. This session delves into our seamless integration of AI, swiftly identifying critical results and improving diagnostic accuracy for lung nodules and fractures. Non-interpretive AI automates report impressions, reducing errors, and our care automation platform boosts patient adherence to follow-up studies.

Vigilance is crucial in working with multiple AI solutions to prevent issues like model drift. We’re dedicated to exploring novel avenues for workflow efficiency as the AI landscape evolves. Our successful deployment involves evaluations, legal reviews, and ongoing dialogues with IT experts to safeguard patient data. Education is key; we’ve invested proactively to equip our radiologist team with skills for AI tools. The session showcases tangible results—a journey of continuous learning enhancing patient care.

Objectives

• Organize insights into a multi-year journey of integrating diverse AI tools into our radiology practice, focusing on their seamless integration into our daily workflow.
• Recognize how AI solutions, from computer-aided detection to non-interpretive AI, improved our workflow efficiency.

• Demonstrate how our proactive education initiatives have empowered radiologists to effectively leverage AI tools.