Mika NewtonEvery podcast appearance, updated as new ones drop

AI is transforming how clinicians handle information, starting with one of the most urgent issues in healthcare: too few providers, too much data, and not enough time. This conversation explores how AI can support physicians by summarizing patient records and medical literature, reducing burnout, and improving clinical decision-making. Hosted by Mika Newton, CEO of xCures (https://www.linkedin.com/in/mikanewton/), the interview features Dr. Spencer Dorn, Vice Chair & Professor of Medicine at the University of North Carolina (https://www.linkedin.com/in/spencerdorn/). He shares powerful insights into AI’s current and future role in healthcare delivery—beyond scribing—to information synthesis, predictive analytics, and ultimately better clinical decisions.

AI scribes are transforming how clinical notes are written, relieving physicians from time-consuming documentation and unlocking more patient-focused care. In this video, we explore what AI scribes really do, how they vary in capability, and which features actually move the needle in clinical practice. Hosted by Mika Newton, CEO of xCures (https://www.linkedin.com/in/mikanewton/), and featuring Dr. Spencer Dorn, Vice Chair & Professor of Medicine at the University of North Carolina (https://www.linkedin.com/in/spencerdorn/), the interview outlines how AI scribes differ by integration depth, impact on workflows, and their role in improving documentation accuracy, coding, and even downstream reimbursement.

AI scribes promise relief from clinical documentation fatigue - but what are their limitations, hidden risks, and true return on investment? In a landscape saturated with new scribing technologies, understanding which tools integrate meaningfully with EHR systems and which merely transcribe surface-level dialogue is critical for healthcare leaders. Hosted by Mika Newton, CEO of xCures (https://www.linkedin.com/in/mikanewton/), this discussion features Dr. Spencer Dorn, Vice Chair and Professor of Medicine at UNC (https://www.linkedin.com/in/spencerdorn/), offering clear insights into how AI scribes impact physician workflow, coding accuracy, and care quality. The conversation examines where current tools fall short, highlights the importance of personalization and contextual summarization, and explores how AI may shift the clinician-patient relationship.

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.

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.

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.

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.

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.

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.

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.

Healthcare data fragmentation remains one of the most pressing challenges in modern medicine. In this episode, Mike Tarselli, Specialist Leader for HCLS Data & AI at AWS, explores innovative solutions with Mika Newton, CEO of xCures. Their platform is revolutionizing how healthcare organizations access and utilize patient data, transforming disconnected, and often complex medical records into actionable insights through advanced AI technology. How did xCures evolve from cancer-specific solutions to a comprehensive healthcare platform? Starting in oncology, xCures developed AI solutions to tackle complex cancer patient management. The platform's success in organizing and analyzing oncology records led to natural expansion across all medical conditions, demonstrating the universal need for better healthcare data management. What makes xCures' approach to medical record management unique? The platform leverages advanced AI and machine learning to convert fragmented medical records into structured, actionable data. This transformation enables healthcare providers to access comprehensive patient histories while maintaining strict HIPAA compliance standards. How does the AWS partnership enable xCures' scalability? By utilizing AWS infrastructure, xCures processes millions of medical records with enterprise-grade security. Their multi-tenant architecture supports rapid growth while maintaining the highest data protection standards required in healthcare. What role does AI play in improving patient record management? xCures employs sophisticated Bayesian models and natural language processing to convert complex medical documentation into searchable, meaningful data. Their semantic embedding technology provides crucial clinical context, enhancing healthcare decision-making. What is xCures' vision for the future of healthcare data access? The company aims to create internet-style healthcare interoperability, addressing critical staffing challenges while improving patient outcomes through enhanced data accessibility and integration. How are healthcare organizations implementing xCures' technology? Telehealth providers and diagnostic companies use the xCures platform to streamline patient care coordination, enabling comprehensive access to patient data during virtual consultations and diagnostic testing through their SaaS solution. Learn more about xCures at https://xcures.com/ Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here.

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.

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 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.

Episode 41: The path to a medical AI holy grail: medical records analysis and automated treatment selection; with Mika Newton, CEO of XCures