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From Practice to Launch: Building an AI Rad Tech Company, with Rad AI Co-Founder Jeff Chang

From Practice to Launch: Building an AI Rad Tech Company, with Rad AI Co-Founder Jeff Chang

Episode Topic: In this podcast episode, we delve into the integration of AI in radiology with Jeff, co-founder of Rad AI, exploring its transformative impact on radiology workflows and patient care. Jeff also shares his firsthand experiences that X Ray Tech can learn from, juggling entrepreneurship with his role as an ER Radiologist, supervising across multiple hospitals. We finally explore the future of radiology in an AI-centric world, discussing industry trends, job opportunities, and essential advice for radiographers navigating this evolving landscape.

Lessons You’ll Learn: You’ll discover how AI can transform the health sector, particularly radiology, and its potential to combat practitioner burnout and improve patient care. Jeff provides insights into how Rad AI makes X-Ray Tech lifes easier by enhancing radiology workflow, automating report generation, and customizing to individual radiologist language preferences. You’ll also learn about the challenges and triumphs of launching an AI-based health tech startup and the importance of continuous learning and staying abreast of technological advancements in the healthcare industry.

About Our Guest: We welcome Jeff Chang, ER radiologist and Rad AI co-founder. At 16, Jeff entered a medical school and became the youngest radiologist in the U.S., specializing in musculoskeletal MRI. During his career, he developed an interest in machine learning, leading him to found Rad AI in 2018. The company aims to boost radiology efficiency and improve patient care through innovative AI solutions.

Topics Covered: During this enlightening episode, we discuss Jeff’s inspiring journey from his early medical years to his overnight shift experiences as a radiologist. We uncover the conception and unique features of Rad AI, focusing on how it automates dictation and customizes to individual radiologists’ language. We also explore the impact of Rad AI on radiology language, workflow, and follow-up care coordination. Furthermore, we delve into user feedback (in particular X Ray Tech), implementation challenges, and future developments. This comprehensive conversation offers invaluable insights into the world of AI-powered radiology, bridging technology and healthcare in an unprecedented way.

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