With Dr. Bernardo Perez-Villa
Dr. Bernardo Perez-Villa, Senior Innovations Engagement Partner at the Cleveland Clinic, shares grounded insights into the true drivers of successful innovation in healthcare. Drawing on his global background in biodesign and commercialization, he discusses the real-world challenges of bringing AI technologies from concept to clinic. From assessing unmet clinical needs to understanding payer dynamics and regulatory bottlenecks, Perez-Villa emphasizes frameworks that distinguish viable solutions from hype. Hosted by Dr. Sanjay Juneja, this conversation unpacks the realities of product-market fit, digital health business models, and how to responsibly scale innovation without compromising on patient safety or financial sustainability.
With Rajeev Ronanki
In this thought-provoking interview, Rajeev Ronanki, CEO of Lyric and bestselling author of You and AI, explores transformative strategies for reinventing payment in healthcare. Rather than incrementally updating legacy systems, Ronanki proposes bypassing them entirely with intelligent, AI-powered intermediaries that enable real-time data sharing between EMRs and payers. The conversation, led by Dr. Sanjay Juneja, covers administrative inefficiencies, trust-building through transparent AI, and the democratization of advanced tools for community care. The dialogue highlights the future of payer-provider collaboration, where AI acts not just as a tool but as a trusted mediator.
With Tom Neyarapally
What’s next for AI in drug discovery and development? Tom Neyarapally, CEO of Archetype Therapeutics, shares his outlook on how converging technologies—from LLMs to spatial omics—are reshaping how we discover and deliver drugs. He emphasizes that while AI tools are evolving rapidly, the integration of diverse data modalities and collaborative innovation between nimble startups and large pharma is essential. Neyarapally also touches on reducing late-stage trial failures and costs—key challenges that AI is finally beginning to address. For stakeholders focused on AI in drug development, this conversation offers both strategic insight and practical optimism.
With David Norris
David Norris, a lifelong technologist and healthcare innovator, unpacks why AI—especially large language models (LLMs)—is gaining rapid traction in healthcare. With decades of progress converging in recent breakthroughs, Norris outlines how AI can now handle tasks from reading faxes to calling patients about lab results. He emphasizes the practical LLM use cases in healthcare that free clinicians from administrative burdens, allowing more time for patient care. This transformation isn't about replacing jobs—it's about restoring the human connection in medicine by shifting repetitive tasks to AI. The conversation brings clarity to the real benefits of AI in healthcare and where it's headed next.
Why AI alone can’t fix healthcare’s data mess. That’s the hard truth revealed by Rajiv Haravu, SVP of Product Management at IMO Health, in this critical interview with xCures CEO Mika Newton.
- Understand why AI isn’t enough to clean clinical data
- Explore the limits of LLMs in healthcare workflows
- Learn about the role of NLP and human expertise
- Discover how precision sets improve data quality
🎥 Watch now to see why real solutions go beyond buzzwords, and what truly enables smarter, safer healthcare decisions.
AI is shaping healthcare decisions, often without us realizing it, by creating digital twins powered by our health data. Jason Alan Snyder from Super Truth explains how this data is used to build these systems, why bad data leads to bad outcomes, and how we can take control of our own information. This eye-opening conversation reveals what’s happening behind the scenes in healthcare and why it impacts all of us.
Healthcare costs are rising, and AI can be the key to fixing the system. Beyond improving patient care, AI has the potential to reduce administrative burdens, streamline workflows, and lower overall costs. Dr. Alister Martin has spent years at the intersection of medicine and social change, exploring how technology can make healthcare more efficient and accessible. This conversation looks at how AI-driven solutions can address both the financial and social challenges facing healthcare.
The future of healthcare is evolving fast, with AI and digital tools transforming patient care. Pelu Tran shares insights on how technology is improving clinical workflows, making healthcare more accessible, and reshaping the role of doctors. We also explore the challenges of integration, the balance between automation and human expertise, and what the next decade could look like for both patients and medical professionals.
AI is transforming healthcare, but where does it fall short? Mika Newton talks with Krish Ramadurai from AIX Ventures about AI’s role in clinical workflows, drug discovery, and full-stack biotech companies. They explore the challenges of integration, the need for domain expertise, and the push for automation in clinical development. Plus, they discuss regulatory hurdles and what investors seek in AI-driven healthcare. Tune in for key insights into the future of AI in healthcare.
Mika Newton talks with Dr. Nigam Shah, Stanford professor and Chief Data Scientist, about AI in healthcare. They discuss AI’s sustainability, regulatory challenges, and the need for localized validation. The conversation explores how AI can improve clinical decision-making, optimize resources, and expand patient access while addressing implementation and data-sharing barriers.
This episode explores AI's role in healthcare, focusing on drug repurposing, data sharing challenges, and regulatory barriers. Mika Newton and Bob Battista discuss how AI can enhance clinical decision-making, improve patient access to treatments, and streamline medical records. They also address privacy laws, governance, and the potential for AI to transform healthcare by making critical knowledge more accessible.
In this episode, Dr. Sanjay Juneja and Dr. Debra Patt discuss AI in healthcare. They cover its applications in cancer care, from improving diagnostics and treatment decisions to enhancing patient education and real-time symptom management. The conversation also highlights AI's potential in drug discovery, reducing healthcare costs, optimizing administrative workflows, and addressing the challenges of data integration and biases in AI systems.
