Being given a cancer diagnosis is one of the worst pieces of news you can receive as a patient. This is often made even more difficult by the fact that choosing a treatment option is rarely simple or easy. Clinicians need to make multiple assessments before they can move forward, and even then it is often difficult or impossible to make unambiguous predictions. That’s where Artera comes in, a company using multimodal AI tests to provide individualized results for cancer patients, which enables clinicians and patients to make personalized treatment decisions, together.

I am joined today by Nathan Silberman, Vice President of Machine Learning and Engineering at Artera, to talk about how Artera’s technology is paving the way for personalized cancer treatment decisions. Join us today, as we get into how Artera is contributing to the cancer treatment process, some of the biggest challenges they face, and how they are addressing these through specifically trained algorithms and robust validation protocols. Be sure to tune in to this important conversation on how Artera is impacting cancer treatment outcomes for the better!

Key Points:

• Background on our guest, Nathan Silberman, and what led him to Artera.
• How Artera is helping clinicians make informed decisions for cancer treatments.
• The role of machine learning in their personalized risk assessments for patients.
• Key challenges they’ve encountered with pathology data.
• How they deal with slide variations through well-trained algorithms.
• Bias in pathology data and what Artera is doing to mitigate bias.
• Their partnerships with academics, clinicians, and oncologists.
• Insight into the variety of approaches they use to validate their models.
• How their tests fit in with clinical workflows and assist doctors and patients.
• The agonizing wait time associated with traditional non-AI testing methods.
• How Artera is providing quick and reliable test results.
• Advice to leaders of AI-powered startups: stay focused on the ultimate goal of patient impact.
• Looking ahead at Artera’s impact in the next three to five years.

Quotes:

“Which therapy to choose is simply not an easy choice. Clinicians would ideally be able to accurately assess a patient's risk of a cancer spreading, or adversely affecting the patient's health in the short term. But often, that's hard or impossible for a clinician to predict.” — Nathan Silberman

“Clinicians have been wanting and waiting for tools that can predict whether or not a therapy will work for that particular patient. This is ultimately where Artera steps in.” — Nathan Silberman

“Rather than wait a month, Artera's test provides the answer within two to three days after the lab receives the biopsy slide. And it is so rewarding to hear from clinicians, and especially patients about the relief we can provide by giving clarity sooner.” — Nathan Silberman

“I think the biggest piece of advice I can give is really just making sure that you're laser-focused on the ultimate goal of patient impact.” — Nathan Silberman

Links:

Artera

Nathan Silberman on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

What if there was a way to revolutionize image-based AI, eliminating the need for extensive prework? In this episode, I sit down with Corey Jaskolski, Founder and President of Synthetaic, to talk about finding objects in images and video quickly. Synthetaic is redefining the landscape of data analysis with its groundbreaking technology that eliminates the need for time-consuming human labeling or pre-built models. It specializes in the rapid analysis of large, unlabeled video and image datasets.

In our conversation, we delve into the groundbreaking technology behind Synthetaic's flagship product and how it is revolutionizing image and video processing. Explore how it utilizes an unsupervised backend to swiftly analyze and interpret data, how it is able to work with any kind of image data, and the process behind ingesting and embedding image objects. Discover how Synthetaic navigates biased data and leverages domain expertise to ensure accurate and ethical AI solutions. Gain insights into the gaps holding AI’s application to images back, the different ways the company’s technology can be applied, the future development of Synthetaic, and more!

Key Points:

• Corey’s background in AI and ML and what led to the creation of Synthetaic.
• Why Synthetaic focuses on processing images and videos quickly.
• How the company leverages ML in its approach.
• Details about image ingestion and embedding processes.
• How the definition of potential objects varies depending on the type of imagery used.
• Explore the role of domain expertise in addressing challenges.
• Hear examples of the technology’s diverse range of applications.
• Recommendations to leaders of AI-powered startups.
• His hope for the future trajectory of Synthetaic.

Quotes:

“We think about the machine learning problems a little bit differently, because we're not labeling data to go ahead and build a bespoke frozen traditional AI model.” — Corey Jaskolski

“We take this very broad view of objects where anything that could be discrete from anything else in the imagery gets called an object, at the risk of basically finding, if you will, too many objects.” — Corey Jaskolski

“We think of RAIC as something that solves the cold start problem really well.” — Corey Jaskolski

“By and large, we're training image and video-based AIs the same way. We need a paradigm shift that really allows AI to be the force multiplier that it can be.” — Corey Jaskolski

Links:

Corey Jaskolski on LinkedIn

Corey Jaskolski on X

Synthetaic

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

What if AI could improve the outcomes of clinical trials by making them more efficient and reducing the number of patients receiving placebos? Well, today’s guest, Charles Fisher is here to tell us all about how his company, Unlearn AI, is creating digital twins to do just that! In this conversation, you’ll hear all about Charles' academic background, what made him decide to create Unlearn AI, what the company does, and how they work within clinical trials. We delve into the problems they focus on and the data they collect before Charles tells us about their zero-trust solution. We even discuss Charles’ opinions of how domain knowledge should be used in machine learning. Finally, our guest shares advice for leaders of AI-powered startups. To hear all this and even find out what to expect from Unlearn in the near future, tune in now!

Key Points:

• A rundown of Charles Fisher’s background and what led him to create Unlearn AI.
• What Unlearn does, what digital twins are, and why they’re important.
• How clinical trials work and how they are used within Unlearn.
• The kinds of data they use and how they tackle these clinical trials using machine learning.
• What a zero-trust solution is and how Unlearn guarantees that their results are accurate.
• Charles shares his thoughts on the role of domain expertise in machine learning.
• His advice for any leaders of AI-powered startups.
• What we can expect from Unlearn in the next three to five years.

Quotes:

“[Unlearn is] typically working on running clinical trials where we might be able to reduce the number of patients who get the placebo by somewhere like – 50%.” — Charles Fisher

“[Unlearn] can prove that these studies produce the right answer, even though they leverage these AI algorithms.” — Charles Fisher

“It's very difficult to find examples where you can actually have a zero-trust application of AI. I actually don't know of another one besides [Unlearn’s].” — Charles Fisher

Links:

Charles Fisher on LinkedIn

Charles Fisher on X

Unlearn AI

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.