AI agents are changing how clinical decisions are made, workflows are optimized, and care is delivered across hospitals, labs, and telehealth systems. Learn how these tools can scale medical expertise, streamline bottlenecks, and improve patient outcomes—without replacing the human element. Hosted by Dr. Sanjay Juneja and featuring expert insights from Dr. Harvey Castro, this interview explains how digital twins, ambient AI, and agentic systems can support clinical judgment, reduce burnout, and personalize care at scale. You’ll also hear practical guidance on whether to build or buy AI tools, how to structure healthcare-specific data, and what integration actually looks like in high-pressure environments. If you’re in healthcare leadership, IT, operations, or product development, this discussion provides frameworks and real-world examples you can act on now.

AI is reshaping rare disease treatments by accelerating drug repurposing, identifying therapeutic opportunities, and unlocking actionable insights hidden in clinical data. Discover how innovative frameworks and technology platforms are helping healthcare leaders, diagnostic companies, and telehealth providers uncover new treatment pathways faster and more efficiently. Dr. Sanjay Juneja (https://sjunejamd.com/) speaks with Dr. David Fajgenbaum (https://www.linkedin.com/in/davidfajgenbaum) about the ethical imperative of using AI-driven drug repurposing to address underserved rare conditions and the growing potential to surface overlooked therapies. Gain practical insights on decision-making, data analysis, and scalable solutions that can impact patient care.

Unlock faster, smarter drug repurposing with open-source data that arms clinicians and researchers with actionable insights. This discussion explores how cutting-edge AI and knowledge graphs are transforming access to potential therapies by ranking 4,000 approved drugs against thousands of diseases - all in record time. Host Dr. Sanjay Juneja speaks with Dr. David Fajgenbaum, Co-founder of Every Cure, to reveal how their platform delivers 75 million computed drug-disease scores directly into the hands of healthcare providers worldwide. Learn how this approach accelerates clinical decision-making, supports evidence-based research, and breaks down barriers to innovative care strategies.

AI prediction is transforming patient care by accelerating how life-saving treatments are identified, repurposed, and delivered to those in need. Discover how cutting-edge data analysis can uncover overlooked therapies and drive faster clinical decisions that impact real-world outcomes. Dr. Sanjay Juneja interviews Dr. David Fajgenbaum, co-founder of Every Cure, to reveal actionable strategies for leveraging artificial intelligence in healthcare systems. Learn how AI-powered frameworks are optimizing treatment selection, guiding clinical workflows, and informing executive decisions across provider networks, telehealth, laboratories, and diagnostics.

The idea that artificial intelligence can uncover life-saving treatments hidden in plain sight is no longer science fiction. In a healthcare system that often favors costly innovation over accessible solutions, using AI to repurpose existing drugs offers a path to faster, affordable breakthroughs. Join Dr. Sanjay Juneja and Dr. David Fajgenbaum, co-founder of Every Cure, as they reveal how AI is accelerating the identification of overlooked therapies for rare and complex diseases. Learn about the critical frameworks behind drug repurposing, how predictive models like Matrix are reshaping disease treatment, and why open-source collaboration could transform healthcare delivery. If you are a physician, healthcare executive, engineer, or healthcare IT leader, this discussion offers essential insights into the future of clinical decision-making.

Accurate clinical insights depend on more than just throwing a large language model at a problem. Data normalization and structured medical concepts shape how AI delivers precision in healthcare coding, clinical decision support, and patient care. Mika Newton, CEO of xCures, and Rajiv Haravu unpack how proprietary medical content, editorial policies, and knowledge graphs provide essential context that LLMs alone cannot offer. Learn why healthcare organizations still rely on medical code sets for reimbursement, accurate ICD-10 coding, and decision-making workflows - and how AI-driven agents may soon accelerate ontology creation, dictionary migration, and terminology mapping. Discover actionable frameworks and expert perspectives on leveraging AI in clinical environments to minimize hallucinations, enhance accuracy, and maintain relevance in a rapidly evolving healthcare landscape.

Data normalization in healthcare isn't just complex – it's mission critical. When a simple lab result like hemoglobin A1C can be recorded under half a dozen different names, clinicians face real obstacles in tracking trends, managing care, and making timely decisions. Mika Newton, CEO of xCures, and Rajiv Haravu, SVP of Product Management at IMO Health, break down why non-standardized data jeopardizes care quality, public health insights, and patient safety. From mismatched lab terms to inconsistent clinical narratives, they explore how definition decay and evolving medical language complicate interoperability and downstream data uses. Learn the frameworks and methodologies IMO Health uses to combat variability – leveraging clinical terminologists, curated content releases, and continuous surveillance of healthcare terminology. Discover how structured and narrative data normalization impacts providers, IT leaders, and healthcare operations.

Healthcare data is messy, inconsistent, and buried in narrative. AI sounds like the solution. Until it isn’t. In this episode of AI and Healthcare, xCures CEO Mika Newton speaks with Rajiv Haravu, SVP of Product Management at IMO Health, to dissect the real-world challenges of data normalization. From inconsistent documentation of basic lab tests to extracting insights from billions of unstructured notes, Rajiv explains why AI alone falls short - and how precision tools, editorial standards, and clinically-informed design can bridge the gap.