AI and Healthcare

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Apr 5, 2025 • 4min

How do you pick the right drug assets?—with Mika Newton

Picking the right drugs is a high-stakes game. Get it right more often than the competition, and the rewards are massive. Miss a few, and you’re out. So how do top investors and biotech leaders make smart bets? Understanding what pharma actually wants, securing multiple key advocates, and strategically managing risk are all part of the playbook. From clinical-stage assets to investor-backed decision-making, this deep dive unpacks how biotech companies position themselves for success—and what separates the winners from the rest.
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Apr 3, 2025 • 14min

What in healthcare AI is real? And what is overhyped noise?—with Mika Newton

The podcast dives into the real impact of AI in healthcare, distinguishing genuine breakthroughs from the hype. It emphasizes the critical need for human oversight and the importance of integrating AI tools with market demands. Experts discuss the necessity of domain knowledge alongside technical skills for scaling solutions. Proprietary data and collaboration are highlighted as keys to enhancing operational efficiency. And while AI brings exciting advancements, the idea of it replacing doctors anytime soon is firmly debunked.
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Apr 2, 2025 • 38min

Why AI Won’t Replace Doctors—But Will Change Healthcare—with Mika Newton [Full Podcast]

Pelu Tran, CEO and founder of Ferrum Health, dives into the revolution of AI in healthcare. He discusses the transformative impact of AI on patient care and clinical workflows, and addresses the fears surrounding job displacement. The conversation highlights the challenges of integrating technology, including costs and data security. Tran emphasizes the importance of balancing automation with human expertise and presents strategies for effective AI onboarding. He envisions a future where innovations can enhance both patient outcomes and medical practices.
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Apr 1, 2025 • 11min

What is AI-enabled predictable and engineerable biology?—with Mika Newton

AI is transforming biotech, making drug development more predictable. From improving drug discovery to tackling the translatability crisis, new advancements are optimizing clinical success rates. Learn why many AI-designed drugs fail, how human data is reshaping the field, and why repurposing shelved assets might be the next big opportunity in pharma.
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Mar 30, 2025 • 13min

Why is continuous health monitoring not a reality yet?—with Dr. Sanjay Juneja

Waiting until cancer is already formed before acting? That approach needs to change. Science now has the tools to detect the earliest warning signs—long before a tumor even develops. Breakthrough technologies like AI-driven monitoring and implantable sensors could transform how we detect disease, tracking subtle biological changes in real time. Instead of periodic tests that might miss critical moments, continuous screening could catch cancer in its earliest whispers—when prevention is still possible. If we already know the steps leading to cancer, why aren't we stopping them? The answers might surprise you.
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Mar 26, 2025 • 41min

Why Doctors Are Drowning in Data – And How AI Can Help—with Dr. Sanjay Juneja [Full Podcast]

David Norris, with two decades in healthcare and a focus on AI technology, joins Dr. Sanjay Juneja to discuss how AI can revolutionize patient care. They tackle the overwhelming challenges doctors face with data and administrative tasks, revealing how AI can ease burnout and enhance doctor-patient relationships. The conversation highlights AI's role in managing lab results, detecting conditions, and streamlining workflows. Ethical considerations and the potential biases in AI applications in medicine are also explored, emphasizing the need for careful implementation.
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Mar 25, 2025 • 8min

How to use AI and longitudinal data collection to surface insights?—with Dr. Sanjay Juneja

Cancer detection is long overdue for a revolution. Current screening methods miss too much, cost billions, and often lead to false positives. But what if we could catch cancer at the very first cell—before it ever becomes a threat? A breakthrough technology could make that possible. AI, continuous monitoring, and implantable devices are reshaping how we detect disease, moving beyond outdated methods toward real-time, 24/7 tracking. Imagine a future where cancer is stopped before it even starts. Is this the next big leap in medicine?
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Mar 24, 2025 • 13min

Why are we waiting for cancer?—with Dr. Sanjay Juneja

Early cancer detection can save lives, yet traditional treatments often focus on late-stage disease. This episode explores the critical need for a shift in approach—studying human cells directly, improving model systems, and building a comprehensive tissue repository to better understand cancer at its earliest stages. Dr. Azra Raza shares insights on key differences in cancer progression between children and adults, along with the ethical and practical challenges of early intervention.
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Mar 21, 2025 • 14min

Is Healthcare Tech Solving The Right Problems?—with Mika Newton

Mika Newton and Dr. Nigam Shah explore whether healthcare technology and AI are addressing the right problems. They discuss the tendency to focus on easy solutions like using language models to respond to patient messages, which may not save time as expected. The conversation highlights the need to redefine goals and use AI for innovative approaches rather than just replicating existing tasks done by humans. They emphasize increasing access and efficiency in healthcare, like using AI for triaging patients and educational interactions, which can free up resources and potentially double service capacity.
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Mar 20, 2025 • 10min

How Do You Evaluate AI's Impact On Patient Care?—with Mika Newton

Mika Newton speaks with Dr. Nigam Shah, a Stanford professor and Chief Data Scientist at Stanford Healthcare, about AI’s role in healthcare. They discuss evaluating AI’s impact on patient care, the challenges of benchmarking AI models, and the importance of using real-world data. The conversation explores how AI can enhance clinical decision-making, the need for well-defined research questions, and strategies for selecting the right data to improve healthcare outcomes.

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