Drug development is notoriously time-consuming and expensive, but what if we could simulate clinical trials before they even begin? Orr Inbar, Co-Founder and CEO of QuantHealth, joins me to explore how his team is doing just that. By simulating trials with AI-native models, QuantHealth helps pharmaceutical companies make better decisions about how to design trials and test drugs.

Orr shares how QuantHealth uses real-world patient data and detailed drug biology to build deep-learning models capable of forecasting patient responses to new therapies. He breaks down their biggest challenges, like the complexities of messy healthcare data, hidden biases, and the importance of domain knowledge when building AI tools for regulated environments. He also shares a key lesson for any AI startup: focus on solving real problems, not just building clever models. Tune in for a fascinating look at how AI is reshaping drug development and what the future of clinical trials could look like!

Key Points:

• Some background on Orr, his parents, and how he founded QuantHealth.
• Key problems QuantHealth is solving as a clinical trial simulation company.
• A breakdown of the biggest challenges facing clinical trials.
• Why we need to improve data-driven trials of drugs.
• How QuantHealth builds their foundation models for trial simulations.
• Examples of the type of predictions their models make in clinical contexts.
• How they use patient and drug data to make predictions and build “digital drugs”.
• Key challenges of working with these different types of data.
• Methods for combating bias, including the use of exogenous data.
• How they incorporate the medical context in model development.
• QuantHealth’s validation process: how they meet rigorous industry standards.
• Orr’s advice to other AI startups on creating value, not just smart models.
• Where you can expect to see QuantHeath in the next three to five years.

Quotes:

“There is a constant desire in drug development and pharmaceutical research to get your hands on more data. This makes sense since it's a very data-driven industry. But at the same time, there was a mismatch there, because there's actually quite a lot of data already out there.” — Orr Inbar

“How do we bridge the gap between the data that we already have and the insights that we need to generate to answer those questions?” — Orr Inbar

“If you take a step back and look at how drugs are being developed today and with an emphasis on clinical trials, we're essentially doing the same things that we were doing 50 years ago.” — Orr Inbar

“Even in a world of GenAI, you can't just snap your fingers and get the solution. It requires a lot of work to structure and harmonize the data.” — Orr Inbar

“Every trial that we simulate, we first go through a data enrichment process where we look for the latest information in terms of research publications, recently completed trials that are relevant to our drug of interest, and incorporate that data into our data sets.” — Orr Inbar

Links:

Orr Inbar on LinkedIn
QuantHealth

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The recent destruction of the Pacific Palisades in Los Angeles was a brutal reminder of why we need robust early wildfire detection systems. Joining me today is Shahab Bahrami, the co-founder and CTO at SenseNet – a company that provides advanced AI-powered cameras and sensors to protect communities and valuable assets against wildfires.

Shahab is passionate about using interdisciplinary research to bridge the gap between machine learning and optimization, and he begins today’s conversation by detailing his professional background and how it led him to co-found SenseNet. Then, we unpack SenseNet and how its technology works, how it gathers data for its AI models, the challenges of relying on images and other sensor data to train machine learning models, and how SenseNet uses multiple sources to detect or define any one problem. To end, we learn why and how SenseNet uses various AI models in a single sensor, how it measures the overall impact of its tech, where the company plans to be in the next five years, and Shahab’s valuable advice for other leaders of AI-powered startups.

Key Points:

• Shahab Bahrami walks us through his professional background and how it led to SenseNet.
• The ins and outs of SenseNet and how its technology works.
• How machine learning fits into SenseNet’s offerings, and how it gathers the necessary data.
• The challenges of working with images and other sensor data to train models.
• How SenseNet integrates information from different sources to zero in on a single anomaly.
• Understanding how it uses multiple AI models to adapt to variations post-installation.
• How the system chooses which AI model to apply and when.
• Shahab describes how his company measures the overall impact of its technology.
• His advice to other leaders of AI-powered startups, and his five-year vision for SenseNet.

Quotes:

“We have one of the most comprehensive wildfire detection solutions in the world, and it is proven by multiple, real-world projects.” — Shahab Bahrami

“Having separate AI models is the solution that we are now implementing.” — Shahab Bahrami

“For the sensor’s AI – because it is a semi-supervised AI, it automatically adapts itself to local conditions. It learns gradually what is normal and what is abnormal, and it is a continuous learning. It won’t stop.” — Shahab Bahrami

“AI changes fast. Every day we have a new AI engine, we have a new model, and leaders, I believe, need to stay updated and make sure their teams have the support and also the resources to keep innovating.” — Shahab Bahrami

Links:

Shahab Bahrami

Shahab Bahrami on LinkedIn

SenseNet

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.

AI is transforming drug discovery by making biological data more accessible and actionable, bridging the gap between complex sequencing data and real-world therapeutic breakthroughs. As Rick Schneider puts it, it's all about leveraging powerful models to “build use cases that matter and bring value.”

In this episode of Impact AI, we hear from the CEO and Co-founder of Helical to find out how bio-foundation models are transforming pharmaceutical research. Rick shares how Helical’s AI platform enables drug discovery by leveraging biological sequencing data without requiring companies to build their own models from scratch. He also reveals the challenges of working with high-dimensional biological data, the power of model specialization for specific therapeutic areas, and the growing role of open-source AI in healthcare innovation.

Whether you're in biotech, AI, or simply curious about the future of medicine, this episode offers invaluable insights into how AI is shaping the next generation of drug discovery. Tune in today!

Key Points:

• Introducing Rick, his engineering background, and Helical’s mission.
• The challenges of leveraging biological foundation models for drug discovery.
• Understanding biological sequencing data and its complexities.
• Key technical challenges: messy datasets, long-range dependencies, and model architecture.
• How Helix, Helical’s mRNA foundation model, competes with industry leaders.
• Three key factors in building biological foundation models: data, compute, and talent
• The shift from narrow AI to general-purpose AI in pharma.
• Benchmarking and evaluating foundation models for different use cases.
• Commercializing Helical’s platform through partnerships with pharma companies.
• Insight into the role of open-source AI in advancing biological research.
• The future of biological foundation models: scaling up for greater impact.
• Rick’s vision for Helical as the backbone of in silico pharma labs.

Quotes:

“The question is, how do I leverage [powerful biological foundation models] and – build use cases that matter and bring value? Helical is building a therapeutic area, an agnostic AI platform that is empowering single-cell RNA and DNA bio foundation models for drug discovery.” — Rick Schneider

“In bio, you can still innovate on the architecture side and not simply [with] the scale of the models. It's not simply by throwing more compute at the models that you get to the very best outcomes.” — Rick Schneider

“Be okay with being different in your approach and accept [that you will] be contrarian to certain things.” — Rick Schneider

Links:

Rick Schneider on LinkedIn

Helical

Helical on GitHub

Helical on Hugging Face

Introducing Helix-mRNA-v0

Helix-mRNA

Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA Therapeutics

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.