Lessons from the regulatory process for medical software for image analysis and AI скачать в хорошем качестве

Lessons from the regulatory process for medical software for image analysis and AI

Speaker: Xenophon Papademetris, PhD Professor, Depts of Radiology & Biomedical Imaging, and Biomedical Engineering, Yale University Prof. Papademetris’ research is in the areas of medical image analysis and the creation of software for medical applications. He is the first author of the recent textbook "Introduction to Medical Software: Foundations for Digital Health, Devices, and Diagnostics" (Cambridge University Press, 2022) and the lead instructor for the freely available companion online Coursera Class "Introduction to Medical Software.” (See https://www.medsoftbook.com for more information on the both the book and the class.) This video is a recording of a seminar that was part of the National Institute of Aging (NIH/NIA) Artificial Intelligence Lecture Series. It was presented on December 12th, 2022. -- Abstract: Artificial intelligence/machine learning (AI/ML) algorithms are currently driving much new research in medical image analysis research. The growth in the number of image analysis publications using such techniques has been exponential. Similarly, in the medical software world, we have also seen an explosion in the number of FDA-cleared standalone medical software devices known as SaMD (Software-as-a-Medical Device), also largely fueled by by AI/ML methods. Recent developments in AI/ML (primarily deep learning) have been surrounded by a large amount of hype and overpromise; a phenomenon that is common in the history of AI. One of the major problems we face is how to avoid overlearning or overtraining an algorithm from the relatively small training datasets available (as compared to what is used for non-medical applications.) Researchers in the field are familiar with how an algorithm’s performance can deteriorate over time as it gets applied to data from slightly different scanners (or even the same scanner after a minor software upgrade), which are both fundamental due to such overtraining. So, while there are many papers advertising exceptional performance, much of this is artificially inflated. The situation is analogous to the p-hacking (reproducibility) crisis seen in other areas of science. In this talk, I will review the medical software regulatory process and recent developments in the use of AI in medical image analysis and present some thoughts as to how some of the procedures used in regulated medical software development (explicit quality procedures, risk classification, risk management, usability engineering, external validation) could be applied to AI/ML to potentially allow this potentially game-changing technology to transform human health. The original seminar announcement can be found at https://nih.zoomgov.com/webinar/regis... -- 00:00:00 Introduction by Dr. Leonid Tsap (NIH/NIA) 00:01:14 Beginning of Talk 00:08:20 An Overview of the Regulatory Process for Medical Software 00:15:19 Machine Learning Background 00:21:44 Some Problems with Current Machine Learning Techniques 00:36:26 Using Lessons from the Regulatory Process to Improve AI Research 00:45:00 Discussion and Conclusions