Lessons from the regulatory process for medical software for image analysis and AI (Dec 12, 2022)

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.



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Webinar: Crossing the Chasm: Growing Tech Professionals into MedTech Professionals (Jan 2023)

This video is a recording of a live webinar organized by Orthogonal that took place on January 26th, 2023. This event featured a discussion on effective onboarding techniques to help MedTech firms get the most out of outside tech hires.  

Can a software engineer or data scientist working in the social media, logistics or cruise ship industries be successful in MedTech? When these new hires are brought on by MedTech firms, it’s with the hope that their experience using modern methods of software development and project management will help MedTech firms Move Faster. But we often see the opposite happen: New hires, frustrated by MedTech's processes, systems, requirements and acronyms, fail to synergize the best ideas from the tech world with the frameworks and constraints of MedTech regulations. 

Before they can help us Move Faster, we need to onboard new employees to the Break Nothing ideal. We need to engage them with not just what we do but also the whys behind it, so they can help us raise the collective knowledge of our teams, bring about culture change and up our software game. 

Orthogonal held a webinar featuring a discussion on how to successfully bridge the chasm between tech and MedTech, and help our industry take advantage of what these tech experts working in other industries can bring to the table to rapidly evolve successful devices. 

This recording includes the 50-minute roundtable discussion, followed by an additional Q&A session with the speakers. The webinar was moderated by Randy Horton, Chief Solutions Officer.

For a summary of the webinar see this blog post:


Expert speakers on this webinar:

*Larkin Lowrey, Engineering Leader & Technologist

*Xenophon Papademetris, Author and Professor, Yale University School of Medicine

*Bernhard Kappe, CEO and Founder, Orthogonal 

*Melissa Gill, Principal Product Owner, Orthogonal

*Randy Horton, Chief Solutions Officer, Orthogonal