AI and Healthcare

AI isn’t replacing healthcare workers - it’s preserving the workforce and extending its reach. From robotic support that reduces physical strain on aging nurses to ambient scribing that eliminates after-hours charting, AI is stepping in where capacity is stretched thin. This conversation explores how AI tools are already shifting care models, enabling clinicians to manage more patients with greater precision and less burnout. Dr. Sanjay Juneja interviews Dr. Harvey Castro, MD, MBA, about practical, near-term applications of AI across clinical environments, including diagnostics, home-based monitoring, and decision support. They discuss real use cases, ethical implications, and a future where physicians spend more time connecting with patients - not keyboards.

Discover how agentic AI is transforming clinical workflows by enabling intelligent digital twins of healthcare professionals that learn, adapt, and act autonomously. This breakthrough supports faster decision-making, reduces bottlenecks, and expands the reach of clinical expertise across provider systems, telehealth, labs, and diagnostic settings. Dr. Sanjay Juneja hosts Dr. Harvey Castro, who explores how agentic systems integrate with wearables, streamline emergency care, and deliver timely medical insights. You’ll learn how scalable AI agents enhance operational efficiency and knowledge-sharing without compromising the human touch.

Healthcare systems face a critical question: build custom AI tools tailored to their workflows, or buy ready-made solutions? This video unpacks the operational, financial, and clinical factors that inform that decision, including insights on cost, customization, integration, and innovation in patient-centered AI. Dr. Sanjay Juneja (https://sjunejamd.com/) interviews Dr. Harvey Castro (https://www.linkedin.com/in/harveycastromd/) to explore the nuances of AI adoption in hospital systems. They break down the value of federated learning, ambient scribe technologies, cultural considerations in care, and how to ensure AI models support rather than replace clinical decision-making. If you're in health system leadership, clinical informatics, operations, or product strategy, this discussion offers frameworks to guide smarter, sustainable AI investments.

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.

Emergency rooms are overwhelmed, not just by medical crises, but by the fallout of systemic failures. A growing number of patients are showing up with issues rooted in poverty, housing instability, and food insecurity. AI is helping change that. By connecting underserved patients to billions in unclaimed public benefits, new tools are offering a path to preventative care that begins outside the hospital walls. Dr. Alister Martin explains how AI is being used to bridge the gap between policy and care, reduce avoidable ER visits, and ease pressure on a healthcare system that spends billions treating conditions that could be prevented. When patients get access to the support they already qualify for, outcomes improve - and so does the bottom line.

As AI becomes a core part of modern medicine, the way we train future doctors may be due for a serious rethink. Empathy and adaptability, not just chemistry and memorization, could define what makes a good physician in the age of augmented intelligence. With language models becoming standard tools for both patients and providers, skills like prompting, critical thinking, and emotional intelligence may soon matter more than traditional academic benchmarks.

What started with a rabbit heart in a physiology lab led to a career focused on preventing strokes through early detection of atrial fibrillation. A Stanford cardiologist shares how that moment sparked a lifelong interest in cardiac rhythms and how today’s wearables can now detect AFib through simple, continuous monitoring, long before symptoms appear. This shift from reactive care to early detection marks a major step forward in heart health, powered by straightforward algorithms and a growing role for AI in predicting cardiovascular risk.

What if your smartwatch could detect a heart condition before you ever felt a symptom? Stanford cardiologist Dr. Euan Ashley reveals how AI and wearables are quietly reshaping the future of healthcare, from spotting silent strokes to redefining what “normal” health looks like. Why do we service our cars and inspect bridges, but wait for our bodies to break down before acting? That question sets the stage for a deep dive into proactive medicine, where tools like the Apple Watch are already catching atrial fibrillation early, and continuous health monitoring could alert us to problems years in advance. Beyond the wrist, AI is transforming everything from clinical documentation to access to specialist care. But big questions remain: Can algorithms be truly equitable? Will personalized prevention ever reach everyone? From ambient AI scribes to the end of “one-size-fits-all” medicine, this is a glimpse into healthcare’s next chapter, where your heart might be talking long before you notice.

AI is only as good as the data behind it, and in healthcare, that data is often messy, outdated, and biased. As systems create digital versions of patients, known as twins, the risks increase when data deteriorates or is used without informed consent. Understanding how data breaks down over time, how it's mislabeled or misused, and why clean, well-governed data matters is essential to creating safer, smarter tools that actually work for people, not against them.

Health data is deeply personal, yet it rarely belongs to the individual. Hospitals, labs, tech platforms, and researchers hold the information that defines our health, often without clear consent or transparency. As data grows more valuable, the people it comes from are often excluded from its benefits. Shifting ownership, improving access, and creating real control are essential steps toward giving individuals the power they deserve over their own health information.

AI systems are no longer just tools, they’re starting to act on our behalf, powered by our data and often without our awareness. These digital twins, built from lab results, genomes, and behavior patterns, are shaping real decisions in healthcare and beyond. When that data is fragmented, outdated, or biased, the risks multiply. Building systems rooted in truth, transparency, and trust is the only way to ensure these technologies serve us - not replace us.

The future of healthcare data could go in two very different directions. On one side is a system where consent is a checkbox, your data is used without your knowledge, and decisions about care, credit, and access are made by algorithms trained on broken information. On the other is a future where individuals own their data, control how it’s used, and benefit from its value. The choice isn’t science fiction, it’s already being made. Now is the time to decide which future we build.

Emergency rooms are stretched thin, and AI might be the key to making them work better, for patients and clinicians. From ambient AI that cuts down on hours of documentation to large language models that surface critical patient history in seconds, new tools are helping doctors focus on care instead of paperwork. These innovations could ease burnout, reduce delays, and transform how decisions are made in the moments that matter most.

We’re entering a future where AI isn’t just supporting healthcare - it’s shaping decisions, influencing outcomes, and acting on our behalf, often without us even realizing it. These systems are becoming digital twins of real people, powered by everything from lab results to behavior patterns, and the implications are massive. Jason Alan Snyder from Super Truth explains how your health data is being used to build digital versions of you - ones that can make decisions without your knowledge. He breaks down why this matters, how bad data leads to bad outcomes, and what it would look like to actually take control of your data. It’s a powerful look at what’s really happening behind the scenes in healthcare, and why it affects all of us.

Tackling healthcare costs requires both human connection and smarter use of technology. Dr. Alister Martin emphasizes the importance of trust, the power of patient navigators, and why AI is quickly becoming essential. With a focus on workforce upskilling and sustainable reimbursement models, there’s a clear path to reducing ER visits, improving outcomes, and making care more affordable.

AI has the potential to transform healthcare, but adoption remains slow. Outdated IT infrastructure, strict data policies, high implementation costs, and concerns around bias and model performance all stand in the way. Scaling AI in hospitals requires more than just promising tools - it demands infrastructure that supports ongoing governance, transparency, and real-world impact.

Healthcare is drowning in inefficiency - 80% of data is noise, and clinicians waste precious time on tasks that don’t improve patient outcomes. But AI is flipping the script. Imagine diagnosing lung cancer in seconds instead of digging through hours of records, or boosting revenue (RVUs) while actually enhancing care quality. Uncover the real barriers to AI adoption and how new platforms are cutting through vendor lock-in to make AI tools accessible in weeks, not months. From radiology to care coordination, AI isn’t just the future - it’s the lifeline healthcare needs today.

Millions of dollars are being wasted in healthcare every year—not because of a lack of resources, but because people aren’t accessing the help that already exists. AI is starting to change that. By streamlining access to underused benefit programs, AI can help patients avoid unnecessary ER visits, reduce hospital strain, and cut costs across the healthcare system. Dr. Alister Martin shares how his work at Link Health is using AI to bridge the gap between patients and public resources, with real-world results backed by recent clinical trials. It’s a powerful example of how smart technology can drive both financial and human impact in medicine.

Healthcare costs are rising, but AI could 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 the healthcare industry.

AI adoption in healthcare comes with complexities, from regulatory hurdles to the challenge of building secure, scalable systems that align with hospital needs. Finding the right balance between innovation and data privacy is key to ensuring these technologies can be effectively integrated into medical environments.

AI is transforming healthcare by streamlining medical workflows, enhancing drug development, and driving more efficient, data-driven solutions. Krish Ramadurai shares insights on AI-native health tech, digital pathology, and the challenges of scaling automation in biotech, highlighting the impact of technology on the future of medicine.

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.

AI in healthcare is evolving fast, but how much of it is real progress—and how much is just hype? While AI-driven tools are reshaping clinical workflows and decision-making, many solutions struggle with integration, regulatory hurdles, and real-world adoption. The real winners? Companies that master proprietary data, streamline physician workflows, and build AI solutions that actually work within the constraints of healthcare. From clinical automation to imaging and diagnostics, the impact is undeniable—but replacing doctors? Not happening anytime soon. Where is AI making a real difference, and where is it just noise? Let’s break it down.

The future of healthcare is evolving rapidly, and technology is playing a bigger role than ever. Pelu Tran shares insights on how AI, data-driven decision-making, and digital tools are reshaping patient care and the way doctors work. From improving clinical workflows to making healthcare more accessible, these innovations are changing the industry in real time. We also explore the challenges of integrating new technology, the balance between automation and human expertise, and what the next decade of healthcare could look like. How will these advancements impact both patients and medical professionals?

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.

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.

Understanding lab results can be overwhelming, but what if AI could help make sense of the data and improve patient care? Dr. Sanjay Juneja and David Norris explore how artificial intelligence is reshaping healthcare—reducing administrative burdens, detecting conditions like iron deficiency, and streamlining workflows. They also discuss how AI is giving doctors more time with patients by handling routine tasks, easing burnout, and enhancing provider-patient relationships. AI is transforming healthcare by improving efficiency, enhancing patient care, and raising important ethical questions about its role in medical practice.

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?

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.

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.

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.

AI is reshaping healthcare, from clinical workflows to drug discovery and the rise of full-stack biotech companies. But what are the real challenges, and where is AI falling short? Mika Newton sits down with Krish Ramadurai from AIX Ventures to break down the complexities of AI in healthcare, the importance of domain expertise, and the push to automate clinical development. They also discuss how startups can navigate the industry’s regulatory landscape and what investors are looking for in AI-driven healthcare solutions. Whether you're in biotech, investing, or just curious about the future of AI, this discussion offers valuable insights.

Join Dr. Sanjay Juneja and Columbia University professor Dr. Azra Raza in an in-depth discussion about the transformative potential of AI and technology in early cancer detection. Dr. Raza shares her insights on how continuous monitoring and innovative technologies could find cancer at the 'first cell stage,' long before traditional methods. Learn about her journey from pediatric oncology to establishing one of the richest tissue repositories, and discover how AI-powered implantable devices could revolutionize the future of healthcare. This episode sheds light on the untapped possibilities in proactive cancer care and the critical shift needed to focus on early detection.

This episode features Nigam Shah discussing the sustainability of AI in healthcare, focusing on challenges in development, validation, and regulation. The conversation explores the limitations of current AI models, the evolving role of governance, and the need for localized validation to ensure accuracy and relevance.

In this episode, Mika Newton speaks with Dr. Nigam Shah, a professor at Stanford and Chief Data Scientist at Stanford Healthcare, about the challenges and opportunities of AI in healthcare. They discuss the sustainability of AI development, the complexities of regulation, and the importance of localized validation. The conversation explores how AI can enhance clinical decision-making, optimize healthcare resources, and expand patient access while addressing barriers in implementation, governance, and data sharing.

This podcast episode explores the intersection of AI and healthcare, focusing on drug development, repurposing, and access to medical treatments. Mika Newton speaks with Bob Battista about the challenges of sharing pharmaceutical data, regulatory barriers, and how AI could enhance clinical decision-making. They discuss real-world data, patient knowledge, and the role of technology in optimizing treatment pathways. The conversation also touches on privacy laws, governance, and the potential for AI to empower patients with better access to medical insights. Viewers will gain a deeper understanding of how innovation and policy changes could improve healthcare outcomes.

Mika Newton and Bob Battista explore how AI could reshape healthcare by giving patients greater control over their medical decisions. They discuss the potential for AI to process vast amounts of clinical data, improve access to relevant treatments, and bridge gaps in health literacy. The conversation also raises important questions about data sharing, patient autonomy, and the future of AI-driven support in medicine.

Mika Newton and Bob Battista discuss how AI is shaping healthcare decision-making. They explore its role in analyzing clinical evidence, updating guidelines dynamically, and personalizing treatment recommendations. The conversation also highlights challenges like regulatory restrictions and data silos that limit AI’s full potential in the industry.

Drug repurposing offers a way to find new treatments using existing medications, but regulatory hurdles and financial disincentives often prevent progress. Mika Newton and Bob Battista examine the challenges of data sharing in the pharmaceutical industry, the high costs of clinical trials, and why companies hesitate to pursue niche indications. They also explore potential policy solutions that could encourage collaboration and make more life-saving treatments accessible to patients.

Mika Newton and Tom Neyarapally discuss how AI is changing the landscape of drug discovery. They examine the role of machine learning, patient data, and computational tools in accelerating the process, reducing costs, and improving the chances of developing effective treatments. The conversation covers how AI-driven screening, drug repurposing, and real-world data integration are helping to overcome traditional challenges in bringing new therapies to patients faster. They also examine the potential for collaboration between AI technology and clinical expertise to drive future breakthroughs in medicine.

Mika Newton and Tom Neyarapally explore how advancements in AI are accelerating the development of new algorithms, improving target discovery, and enhancing high-throughput screening. The conversation highlights the challenges and opportunities in aggregating innovative technologies, the convergence of AI tools in healthcare, and the potential for personalized and economically viable drug development.

Explore how AI is transforming drug discovery by enabling the rapid screening of billions of molecules and identifying new uses for existing drugs. This approach integrates patient data with advanced tools to predict effective treatments for diseases with unmet medical needs, significantly reducing the time and cost of development.

In this episode, Dr. Sanjay Janeja 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. Together, they emphasize the importance of collaboration between clinicians and digital tools to deliver better, more efficient patient care.