Clarified Precision Medicine is reshaping cancer care—and we’re honored to support them through the Arionkoder Reshape Health Grants.
Led by CTO Daniel Rotroff, their team is using AI to guide treatment decisions based on each patient’s molecular tumor profile, aiming for more effective and timely outcomes.
Our collaboration focuses on:
- Creating an AI-powered dashboard to evaluate how treatments are working.
- Surfacing insights from complex datasets across providers and pharmaceutical companies to identify care gaps.
We’re excited to help accelerate innovation in precision oncology. Join us to see how this collaboration grows!
See the video transcript below:
My name is Daniel Rotroff, and I’m the Chief Technology Officer for Clarified Precision Medicine. Clarified Precision Medicine aims to make sure that all cancer patients,receive the right therapy for them at the right time during their course of treatment.
What Clarified Precision Medicine does is it basically create a scalable solution so that we can offer that expertise and that therapeutic guidance for patients at a level that can meet the demand for all patients with cancer.
Right now, when a patient goes, they send a sample of their tumor or a lot of times the blood, will be sent off for a liquid biopsy and it’ll go to a sequencing lab. We work with any of the major labs in the country, and their tumor will get sequenced and a report comes back with all the different mutations that they found. What we do is we take all those mutations, we ingest them into our knowledge base, and we identify therapies that are known to act or potential resistance have resistance to those individual mutations, we then have our expert panel, precision medicine expert panel, review those and identify which therapies are the best for that patient. And we do this by leveraging AI to sort of scale that solution, offering the recommendations to the reviewers so that it’s quick and easy and accessible to them.
So I think one of the things that we’ve needed really is we’ve accumulated quite a lot of data, and this data can help us gain insights into, the clinical practices that we’re working with. And they can also help us understand some of the gaps that exist either within a certain cancer type or across cancer types. And that’s where we wanted to work with Arionkoder to develop tools to help us, you know, really take a deeper insight into the data that we’ve collected, identify things like how often patients are switching therapies based on the results of their sequencing result.