Impact AI Reducing Radiologist Burnout with Jeff Chang from Rad AI
Burnout in the medical field is a significant and pervasive problem that affects healthcare professionals across various specialties and levels of experience. In this episode, I explore the impact of artificial intelligence on the field of radiology. My guest, Jeff Chang, the Co-founder and Chief Product Officer of Rad AI, shares his insights into the transformative power of AI in addressing critical challenges in radiology (like burnout) and improving patient care.
Discover Rad AI's groundbreaking products, including Rad AI Omni Impressions and Continuity, driven by powerful machine learning, and learn how they seamlessly integrate into clinical workflows. Jeff also speaks about measuring the impact of their products, exciting future applications for their products, Rad AI's vision for the future of global diagnostic care, and much more! Don't miss this deep dive into AI's potential in revolutionizing radiology and improving patient lives worldwide with Jeff Chang from Rad AI!
Key Points:
- Background on Jeff and the overall mission of Rad AI.
- Insight into the next-generation products that Rad AI offers.
- How machine learning plays a central role in their products.
- Training data and the use of transformer models in Rad AI's solutions.
- Certain limitations and hurdles of working with radiology reports.
- Ensuring product integration with existing clinical workflow.
- Exciting future applications for AI and machine learning in the space.
- Ways that Rad AI measures the impact of its technology.
- Essential advice for leaders of AI-powered startups.
- Upcoming products from Rad AI and the company’s future impact.
Quotes:
“Every one of our products is centered around the latest machine learning transformative work.” — Jeff Chang
“There is absolutely zero change to the existing workflow. That makes it really easy for radiologists to adopt without having to change anything that they currently do.” — Jeff Chang
“The more you streamline both deployment and the training process, getting radiologists or your users used to using the product, the easier it becomes and the more time you save for the radiologist.” — Jeff Chang
“Because of the post-processing, because of the specific training on the radiologist’s historical reports, [our models] are much more accurate than the current state of the art [LLMs] for the applications that we currently provide.” — Jeff Chang
Links:
Resources for Computer Vision Teams:
LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.