Gil Elbaz, Datagen CTO and co-founder, interviewed Idan Bassuk, VP AI at Aidoc, about computer vision, natural language processing, and AI explainability in the medical field. During the episode, Idan discusses the challenges that anyone building an AI team for scale should know.
AI Algorithms and Software Engineer, Idan Bassuk, joined Aidoc four and a half years ago as VP AI. He now leads a 90-person team focused on all aspects of AI, from dataset development to algorithmic research to deployment of continuous monitoring in over 500 medical centers worldwide. Idan spent ten years in the IDF before joining Aidoc as its first employee.
This transcript has been edited for length and clarity. Listen to the full podcast here.
What does Aidoc do?
Idan Bassuk: Aidoc is the leading provider in AI for medical imaging like CT scans, X-rays, and MRIs that are interpreted by radiologists. Radiologists specialize in reading, interpreting and diagnosing, based on these medical images.
We have built a system around the concept of always being on. It means that we are always running in the background. We don’t wait for the radiologist to send us a question about a scan, but we are listening to the databases of the hospitals. In many cases, we are even connected directly to the scanners themselves. And once this new scan gets acquired, we automatically identify it, analyze it with AI algorithms to detect different types of medical conditions, such as brain hemorrhages, spine fractures, and strokes. These are critical and life threatening medical conditions. We often do this analysis before the radiologist even opens the scan. And our goal is to drive the radiologists to get to the most important and the most critical patients earlier.
How does Aidoc save lives?
Idan Bassuk: We do retrospective studies on years of data, depending on what needs to be done to be statistically significant. And we’ve done it on millions of scans already, even as part of research. Research is conducted independently by these medical centers, and they’ve published dozens of academic papers on it.
We have seen in this controlled research that we are improving the patient outcomes by reducing the missed detection rate by radiologists. And we have a very good ratio between our sensitivity, or what is known as the recall, and the number of false positives that we provide.
One of the great things is, really on a daily basis, we get emails or WhatsApp messages from doctors that are using our products giving us examples of patients whose lives we saved or helped save.
How do you build your team?
Idan Bassuk: I lead the AI group at Aidoc, including the concept of the AI group. It’s not only a group of the algorithm, engineers or researchers, but it contains all the teams that are responsible, in any way, for developing the AI or bringing it to production to the real world.
In parallel to this AI Operations Center, this group also contains several other teams, each of them part of the concept of holistically attacking the AI challenge from many directions. We have a data engineering team that is responsible for data engineering platforms. enabling data mining, enabling building the data sets and the platform, which enables us to develop the datasets across dozens of types of data and scans and reports and medical records, etc.
Our data engineering team builds platforms that enable us to utilize the data to the best extent possible. There is a notion, in AI, that more data is better, but more data is not necessarily better. It’s not interesting. It doesn’t teach the algorithm anything new. Most of the patients are healthy. But our goal is to train on the smallest datasets possible, which contains the most interesting scans and not on the largest dataset possible. We think that that’s not the correct KPI.
Listen to the full podcast here.
That’s one team. Another team is the dataset engineering or dataset development group. It’s a generalization of the concept that in many companies is just called data annotation, but we think that building a dataset on which you train and test your AI, especially in the challenges of the medical world, is much beyond the notation, since you need a trained radiologist to do it.
You need people who understand not only the medical conditions, not only the hemorrhages and where to find them, but also the data variability, the physical properties of the scans, and our customers’ needs, in order to choose the most important and most interesting scans for the algorithm.
Working Agile in AI
Idan Bassuk: Being able to reproduce your algorithm, and the infrastructure around it, is a prerequisite for working agile in AI. What is agile? To be agile in AI, you need to ship it to the customer, get feedback, and then spend weeks or months resuming where you left off.
You don’t want to develop the algorithm that you will have five years from now and only then ship it to the customer. You want to get feedback from your customer on the premature version to see what they think should be improved the most. But if you ship it to the customer and get feedback, and then you need to invest several weeks or months, just to be able to continue where you left off, it’s an impediment to being agile in AI, because it will encourage you to make longer iterations because resuming is more painful.
This specific aspect of agile working iteratively, I think is a highly important pattern; not having impediments that encourage you to make the iterations longer than what makes sense from the product perspective.
I think that generally it’s a good idea to take the framework of agile and scrum or something similar to that, but not stick to it too tightly, by the way, not necessarily only in AI, but in general, as it will be adapted to the needs of your organization.
Getting started in computer vision
Idan Bassuk: I personally think that the most important thing for a junior engineer, someone relatively new to the industry, is not what project you are working on or what company you are working in, or what domain you’re working on, it’s the quality of the people that you will be working side by side with.
And by the way, even if you will not necessarily do the most sexy tasks in this team, you will have the opportunity to watch experienced engineers and how they tackle complex problems and discuss it with them.
I think you will learn much more from it in many cases. As talented as you are, I think it’s very valuable for most people to have someone to learn from, not necessarily a formal mentor. I think that just the people on the team can really change your pace of growth.
Listen to the full podcast here.