Explore the regulatory process for medical software in image analysis and AI through this 51-minute seminar presented by Professor Xenophon Papademetris from Yale University. Gain insights into the challenges and opportunities in medical image analysis research, particularly focusing on AI/ML algorithms and their applications in standalone medical software devices. Examine the issues of overlearning and overtraining in AI algorithms, and discover how procedures from regulated medical software development could potentially improve AI/ML technology in healthcare. Learn about quality procedures, risk classification, risk management, usability engineering, and external validation in the context of medical software development. Delve into an overview of the regulatory process, machine learning background, current problems in ML techniques, and potential solutions derived from regulatory practices.
Lessons From the Regulatory Process for Medical Software for Image Analysis and AI
Overview
Syllabus
Introduction by Dr. Leonid Tsap NIH/NIA
Beginning of Talk
An Overview of the Regulatory Process for Medical Software
Machine Learning Background
Some Problems with Current Machine Learning Techniques
Using Lessons from the Regulatory Process to Improve AI Research
Discussion and Conclusions
Taught by
Yale Radiology and Biomedical Imaging
