AI and Healthcare
ClaimedAI and HealthcareClaimed
Episodes
AI and Healthcare
Claimed
Episodes of AI and Healthcare
Mark All
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 inte
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
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
AI is only as good as the data behind it, and in healthcare, that data is often messy, outdated, and biased.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 c
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, th
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 healthca
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
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 seco
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
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
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 r
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
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 progr
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 a
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 th
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 s
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 actuall
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
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 impro
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
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 impla
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 bur
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 breakthrou
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 comprehe
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 ti
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