Impact AI Making Cancer Treatments Affordable with Laura Kleiman from Reboot Rx
Giving generic drugs a new life in oncology is a game-changing strategy for developing new and affordable treatment options for cancer patients. But it would take years to review the thousands upon thousands of published research studies on non-cancer drugs tested as cancer treatments to identify the most promising candidates. Luckily, nonprofit health tech startup Reboot Rx is stepping in to solve this problem!
I spoke to Founder and CEO, Laura Kleiman about how her company is fast-tracking the development of affordable cancer treatments using AI technology. Working with a team of biomedical and clinical scientists, Reboot Rx uses machine learning and natural language processing to analyze large volumes of scientific literature, identify the most viable drugs, and develop pathways to generate definitive evidence and change the standard of care so that patients can benefit from them.
In this episode, you’ll learn more about Reboot Rx’s multi-pronged approach and the challenges that come with processing such large volumes of data. Plus Laura shares her advice for tech leaders looking to solve problems that have a meaningful societal impact.
Key Points:
- A look at Laura’s background and the personal story of what led her to create Reboot Rx.
- The important work Reboot Rx does to repurpose generic drugs for the treatment of cancer.
- An example that illustrates the role that machine learning plays in this process.
- Challenges that come with analyzing this type of data.
- How Reboot Rx’s machine learning developers collaborate with healthcare experts to ensure that their models are effective.
- Advice for leaders of AI-powered startups and nonprofits: choose problems that matter!
- How Reboot Rx is working to make their AI technology scalable.
Quotes:
“Patients need both more effective but also more affordable treatment options. Reboot Rx is giving generic drugs a new life in oncology, taking drugs that are already available to treat other non-cancer indications and repurposing them for the treatment of cancer.”
“We use large language models to be able to process [large volumes] of scientific literature and predict which of these 600,000 studies are most likely to be relevant and extract key information about each of these studies.”
“There's so much opportunity for the use of AI and machine learning right now. I would encourage other leaders in the space to choose problems to solve that can have a meaningful societal impact.”
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