Using AI to Improve Clinical Trial Access for Those With Lung Cancer

Dr. Josh Sabari, an assistant professor in the Department of Medicine at NYU Grossman School of Medicine and CURE’s editor-in-chief, sat down with Max Doppelt at the 2025 IASLC World Conference on Lung Cancer to discuss clinical trial access for lung cancer patients.

Doppelt, who is a lung cancer patient advocate, as well as the founder of the Stealth AI Health Startup, shares his personal experience navigating trials for his mother, who has an EGFR Exon 20 mutation. He also goes on to explain how it inspired him to develop an AI-driven tool to match patients with the most suitable trials.

The pair also explore the challenges in trial enrollment and the potential of emerging therapies to offer hope for patients diagnosed today.

Transcript

Sabari: Hello. I'm Dr. Josh Sabari, a thoracic medical oncologist from NYU Langone Health's Perlmutter Cancer Center. I'm also the editor-in-chief of CURE magazine, and I'm here with Max Doppelt, a colleague and friend. Max, what brings you to this conference?

Doppelt: Well, I'm here to really learn more about improving clinical trial access. I'm a caregiver myself. My mom was diagnosed with late-stage lung cancer about two years ago, and thankfully, today, she's still battling. So, I'm here for a number of reasons. For one, I want to really understand the landscape and what the emerging therapies are in this area.

She has a unique EGFR Exon 20 mutation, and the latest advancements coming out in that space have been truly amazing. So I'm really excited to be here to learn more about the emerging therapies, but also, more broadly, to dedicate my career to improving clinical trial access for other patients.

Sabari: You have an amazing story and an amazing journey, both in business and tech, and now you're really entering the oncology space. What would you tell someone newly diagnosed in 2025? What advice would you give them? And how would you give them hope with this diagnosis?

Doppelt: I think there's so much that's really encouraging right now. When my mom was first diagnosed, ChatGPT and this entire AI wave was really up and coming, and those technologies truly enabled me to be the best version of a caregiver that I could be. We have an amazing team of oncologists, and since we live in New York, we obviously go way back. But being able to leverage everything from my tech background to help my mom navigate her care, and now getting involved in patient advocacy and building tools to help other patients, is where my passion is.

Sabari: Now, yes, you're a true patient advocate. You're here at conferences. I see you at many conferences. You know a lot more than many of the doctors I actually talk to in clinical practice. Within the EGFR Exon 20 space, I know you're working on a really cool product — a model that helps patients match to clinical trials. Tell me more about it. Where did that come from? How did this come about?

Doppelt: Sadly, it came about through personal experience. Clinical trial access is a very well-known problem. A number of companies have tried to target and build solutions in this space. Earlier this year, when my mom's condition metastasized to her brain, there was no standard of care available to her, so we turned to clinical trials. The institutions were able to help in certain ways, but we had a really hard time navigating and understanding what trial was best for her. Then, actually going through the process of accessing and getting onto a trial was a nightmare.

It would take several weeks to determine eligibility and go through screening. Part of what I really want to focus on is introducing transparency into the process.

Sabari: Yeah, I agree. Clinical trials are critical in moving the field forward by studying new medicines, but the process is very clunky. The whole process is so difficult to get people enrolled and onto trials. There are so many inclusion and exclusion criteria. What was your experience like navigating this trial landscape, and how do you hope to change that in the future?

Doppelt: So, ClinicalTrials.gov is publicly available, but it only captures a subset of a study's true protocol. That was a learning experience for me, because I would go through the trial search engine and find trials over the course of hours — it's such a complex piece of software to navigate. Then, once we found trials that she was a great candidate for, we went through screening. I was ultimately so confused as to why she was failing screening for multiple trials. It's because, especially in the oncology and rare disease space, these protocols have gotten so complex, and there are so many manual processes in determining whether a patient passes.

Part of what I'm building is a tool that utilizes AI to leverage a patient's medical records and determine, in closer to real time, the best matches for them. This will introduce transparency for the patient and honestly help the sponsors as well, because these are critical drugs, and patient recruitment and getting to full capacity and enrollment is a huge challenge for them.

Sabari: Yeah, it's such an important unmet need. I've seen early versions, and it's really exciting to be able to take patient data and understand what trials would be the best fit for them. There are lots of different companies and products available. I think bringing AI into this space addresses a huge unmet need, and it's a great opportunity to help people diagnosed with lung cancer.

Max, thank you for joining us, for telling your story, and for everything that you do for the community.

Transcript has been edited for clarity and conciseness.

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