AI and Healthcare

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.