In this episode, I talk with John Bertrand, CEO of Digital Diagnostics, about autonomous diagnostics. Digital Diagnostics transforms the quality, accessibility, equity, and affordability of healthcare with AI-powered diagnostics. They developed the first FDA-cleared autonomous AI system.

Quotes:

“So we look for diagnostics where there’s an established understanding of what the disease is and there’s a gold standard as to how to measure that.”

“We’ll naturally start with an area where positive and negative is a very binary decision that is almost mathematically derived.”

“It goes back to picking the right types of disease states to make sure that the gold standard already exists.”

“How do you take images that have different coverage of the retina but make sure that you piece them together in a way that the processing part of the system is getting a consistent image that they’re looking at every single time so that the algorithm remains consistent and we don’t have to have different algorithms per vendor that we’re interacting with.”

“We’re pretty proud of the fact we’ve been able to do that first kind of assistive feedback for the provider.”

“We want every single patient, regardless of their background, to receive consistent quality of diagnostic output. What that means is that we actually have to build our training data sets as well as our clinical validation studies and trials to take into account a diverse population set.”

“Continuous learning versus locked algorithms is another key factor. . . Would you really want that algorithm to adjust to the most recent data it’s seeing, thinking it’s attempting to become more accurate, when in fact it’s really more optimizing for the ethnicity of the folks in that particular region, the sun rises on the east coast to the United States, everybody further east goes to bed. Now the algorithm’s been indexed towards another group from a ethnicity perspective, that’s no longer representative of where the testing’s being done as the sun rises in New York.”

“How do we ensure that we create confidence with regulators, with providers, and with patients that we’ve actually thought through this?”

“We can literally break down for you what the computer saw, why graded it out what it did, and why it gave you the results it did.”

“Your algorithm should be explainable so people trust the technology, understand how it works.”

“Also explainability helps you drive better accuracy and that you understand why you’re getting the result that you’re getting with the black box approach.”

“You really want to work within the healthcare system when you’re building these types of businesses.”

“If you’re going to chart that course and really carry through to fruition, your vision of building an algorithm that impacts patient lives, I think you really need to center the culture of the business around a commonly shared vision for the mission of what you’re trying to do.”

Links:

Digital Diagnostics