From Interview #97
With Dr. Ben Rosner
Dr. Ben Rosner, a practicing clinician and digital health thought leader, discusses the evolving role of generative AI in medical education. As the faculty lead for AI innovations at UCSF School of Medicine, he outlines real-world pilot programs using AI to automate administrative tasks, enhance diagnostic training, and even generate personalized tutor bots. However, he raises critical concerns about “de-skilling”—a phenomenon where overreliance on AI can erode core clinical competencies. Drawing parallels from aviation and colonoscopy procedures, Dr. Rosner explains why educators must tread carefully. This insightful clip explores challenges in medical education AI integration and raises pressing questions about how AI can enhance learning without undermining human expertise.
From Interview #89
With Harvey Castro
Harvey Castro, Partner and Medical AI Advisor at Aexa Tech, discusses the strategic decision hospital systems face when integrating AI: whether to build custom solutions or buy from vendors. He explains that resource availability, cost efficiency, and the need for customization drive these decisions. Some hospitals benefit from vendor solutions due to scalability, while others save costs by building tailored tools, such as ambient scribes, using open-source models. Castro highlights the importance of involving healthcare professionals and patients early to ensure adoption and success. He also explores federated learning as a path for hospitals to train models collaboratively while preserving data privacy.
From Interview #89
With Harvey Castro
Harvey Castro, Partner and Medical AI Advisor at Aexa Tech, explores how agentic AI could transform clinical workflows for healthcare providers. He envisions digital twins of physicians that replicate their knowledge, creativity, and warmth, enabling autonomous consultations even during off-hours. These AI agents could collaborate, interpret medical records, and provide decision support while still requiring human oversight to validate outputs and prevent errors. Castro also imagines agentic AI integrated with wearable devices and smart glasses to deliver real-time updates, mentorship-like knowledge sharing, and global best-practice dissemination. He argues that agentic AI can reduce bottlenecks, lower costs, and improve efficiency, creating scalable expertise pools for providers.
From Interview #89
With Harvey Castro
Harvey Castro, Partner and Medical AI Advisor at Aexa Tech, explains how AI and robotics can help address the growing healthcare workforce shortage. He highlights the role of robots in assisting with physically demanding tasks such as lifting patients, reducing strain on aging nurses. He describes AI’s potential to supplement clinicians by analyzing patient data, supporting diagnosis, and enabling family doctors to handle cases typically referred to specialists—what he calls the 'great shift.' Castro also envisions in-home monitoring tools to support medication adherence and continuous care, shifting more healthcare delivery from hospitals to homes. He emphasizes that AI will not replace providers but will enable them to focus on the sickest, most complex cases.
From Interview #89
With Harvey Castro
Harvey Castro, Partner and Medical AI Advisor at Aexa Tech, offers practical advice for doctors learning AI. He emphasizes treating AI education like medical training—requiring lifelong learning, discipline, and passion for patient care. Castro encourages clinicians to follow diverse voices in the AI space, compare perspectives, and stay updated through platforms like LinkedIn, YouTube, and podcasts. He envisions a future where interactive AI tools, like conversational podcasts, keep doctors current with the latest research. For Castro, the key is to channel passion for patients into motivation for adopting AI tools responsibly.
From Interview #91
With Dr. Spencer Dorn
Dr. Spencer Dorn, Vice Chair and Professor of Medicine at UNC, breaks down the rapidly evolving market of AI scribes in healthcare. He explains that while scribes solve a clear pain point—time-consuming note-taking—they vary widely in capabilities. Basic scribes transcribe patient-doctor conversations into notes, while advanced versions integrate with EHRs to provide contextual awareness, summarize records, and even suggest diagnostic codes. Dorn emphasizes that scribes are especially effective for capturing patient history (HPI), but less so for assessments and care plans. He notes the potential ROI through improved reimbursement coding, physician wellbeing, and enhanced patient experience.
From Interview #91
With Dr. Spencer Dorn
Dr. Spencer Dorn, Vice Chair and Professor of Medicine at UNC, discusses how AI can automate critical healthcare tasks to address systemic workforce shortages. While AI scribes represent the first wave, Dorn highlights summarization of clinical records and medical literature as the next major frontier. He emphasizes that healthcare is fundamentally an information processing discipline, yet the volume of records and literature is overwhelming. AI-powered summarization tools can reduce cognitive load, improve decision-making, and decrease burnout. Looking ahead, Dorn identifies predictive algorithms as a long-term opportunity, enabling physicians to forecast patient risks and apply judgment to mitigate them.
From Interview #91
With Dr. Spencer Dorn
Dr. Spencer Dorn, Vice Chair and Professor of Medicine at UNC, outlines the risks of using AI in healthcare. Beyond well-known issues like privacy breaches, hallucinations, and bias, he warns of less-discussed risks: AI could paradoxically make clinicians’ work harder instead of easier, eroding promised efficiency gains. He highlights the danger of diminishing critical thinking skills as tasks like note-writing and summarization are offloaded to machines. Most importantly, Dorn stresses that AI could harm the human relationships at the heart of medicine if bots replace authentic communication. He also raises unresolved questions about legal responsibility in the absence of clear regulations, noting that clinicians may ultimately be left accountable for AI-driven errors.
From Interview #97
With Dr. Ben Rosner
Dr. Ben Rosner, a hospitalist and digital health researcher at UCSF, explores the promise and pitfalls of AI-generated discharge summaries. In this wide-ranging discussion, Dr. Rosner explains how LLMs can reduce administrative burden, improve communication at discharge, and potentially enhance patient safety—if implemented thoughtfully. He shares findings from his JAMA-published study evaluating LLM-drafted summaries and outlines how these tools perform against physician-written counterparts. The conversation expands into the risks of de-skilling, challenges of AI trust, and the need for systems like "LLMs as juries" to monitor AI-generated clinical documentation. Rosner also reflects on AI’s broader impact on medical education and the role of emerging roles like Chief Health AI Officers.
From Interview #81
With Krish Ramadurai
Krish Ramadurai, Partner at AIX Ventures, outlines his strategy for selecting drug assets in a crowded and competitive pharma landscape. He emphasizes starting with deep conversations with key opinion leaders (KOLs) to identify areas that have broad consensus among industry experts. Ramadurai recommends diversifying assets to avoid over-reliance on a single program, advocating for at least one clinical-stage asset supported by multiple preclinical backups. He also stresses the importance of founder engagement and having clear out-licensing strategies for assets that don’t align with the founder’s passion or company focus.
From Interview #76
With Dr. Debra Patt
Dr. Debra Patt outlines the many ways AI can address the financial burden on healthcare systems, especially in oncology. She highlights AI-powered patient education tools that prevent costly ER visits, AI-enabled drug discovery methods like AlphaFold that streamline development, and smarter clinical trial designs that reduce waste. By targeting precise molecular mechanisms, AI offers the potential for more effective, less toxic treatments. Patt emphasizes the dual benefit of improving patient outcomes while being responsible stewards of healthcare spending.
From Interview #79
With Dr. Nigam Shah
In this conversation, Dr. Nigam Shah, Co-founder of Atropos Health and Chief Data Scientist at Stanford Healthcare, challenges how the healthcare industry applies AI. He warns against the 'Turing trap'—using AI to replicate tasks clinicians already perform—instead of leveraging it to reimagine workflows. Shah shares examples of AI applications that could meaningfully expand patient access, like triage tools that reduce unnecessary visits. He also highlights how misaligned incentives block data sharing, which limits innovation despite having the necessary technology. This discussion addresses both healthcare tech trends and the urgent need for better AI integration strategies.
From Interview #85
With Jason Alan Snyder
Jason Alan Snyder breaks down the challenges of poor data quality in healthcare, particularly the issue of data decay. He explains that while AI models depend on accurate and relevant data, much of today's healthcare information is outdated, incomplete, or biased, leading to flawed recommendations. Snyder identifies four main types of data decay—temporal, structural, contextual, and semantic—and highlights the risks of semantic manipulation in shaping medical terminology for market advantage. He advocates for clean, consent-based, and personally controlled data as the foundation for ethical, precise, and effective healthcare AI.
From Interview #87
With Rajiv Haravu
Rajiv Haravu outlines the complex challenges in healthcare data normalization, emphasizing variability in clinical documentation and the real-world consequences of non-standardized data. He explains how differences in terminology, context, and documentation style can lead to information loss, affecting patient care, research, and public health. Haravu also discusses 'definition decay'—how medical terminology and meaning evolve over time—and highlights IMO Health’s approach of constant surveillance, expert curation, and regular content updates to maintain accuracy.
From Interview #85
With Jason Alan Snyder
Jason Alan Snyder tackles the complex question of health data ownership, arguing that individuals—not corporations—should control their medical information. Despite legal rights under HIPAA, patients face significant barriers to accessing their own data, which is often siloed, unstructured, and inaccessible in a useful form. Snyder notes that providers, labs, pharma, and tech platforms profit from patient data while offering little in return to its source—the patient. He calls for systems that unify fragmented data, create portability and usability, and enable individuals to profit from their health information ethically and securely.
From Interview #81
With Krish Ramadurai
Krish Ramadurai explains how AI is transforming biotech from a high-risk guessing game into a predictive science. He discusses the need for diverse, human-based datasets to avoid the pitfalls of biased models that lead to drug failure. Ramadurai highlights the value of rescuing shelved drugs using chemo-informatics to tweak their properties for renewed clinical viability, as well as investing in companies with strong data moats. By combining robust biology with advanced AI, he envisions reversing biotech’s current 95% failure rate and accelerating time to market for safe, effective therapies.
From Interview #81
With Krish Ramadurai
Krish Ramadurai breaks down the reality of AI adoption in healthcare, separating transformative applications from overhyped promises. He emphasizes that success comes from domain expertise, frictionless integration, and solving real workflow problems—not from replacing physicians. Generative AI has proven effective in clinical workflow automation, like dictation and EHR integration, but faces limitations in complex decision-making domains. Ramadurai warns against 'pilot purgatory' and stresses the importance of demonstrating clear ROI, enterprise readiness, and alignment with the needs of patients, payers, and providers.
From Interview #84
With Dr. Alister Martin
Dr. Alister Martin shares how AI is transforming emergency medicine by tackling two of the ER’s biggest challenges: documentation and rapid patient assessment. He describes how ambient AI systems automatically capture doctor-patient conversations and generate accurate clinical notes, freeing clinicians from hours of charting. In high-pressure moments, large language models can instantly summarize a patient’s medical history, allowing physicians to make informed decisions within seconds. These tools not only save time and reduce burnout but also improve care quality by ensuring no critical detail is overlooked.
From Interview #78
With Bob Battista
Bob Battista explains how AI can transform healthcare decision making by creating personalized treatment guidelines in real time. By integrating patient chart data with the vast body of published clinical evidence, AI can identify the most effective treatment paths for each individual. While access to proprietary data inside pharmaceutical companies could further enhance these capabilities, even existing public data offers tremendous potential. Battista emphasizes that applying best practices earlier in the care process could improve outcomes and save significant costs, especially if tailored to at-risk patients.
From Interview #85
With Jason Alan Snyder
Jason Alan Snyder explores two possible futures for big data in healthcare: a dystopia where patients lose all agency and a utopia where digital twins empower individuals. In the dystopian scenario, consent is reduced to a checkbox, algorithms trained on flawed data determine access to care, and patients have no visibility into decisions. In contrast, the utopian vision puts patients in control of their data, with digital twins serving as personal advocates that guide care, warn early, and even generate income. Snyder argues that achieving this future requires courage, will, and—most importantly—better healthcare data quality as the foundation for trustworthy AI tools.
From Interview #87
With Rajiv Haravu
Rajiv Haravu, SVP of Product Management at IMO Health, explains why medical code sets remain essential despite the rise of large language models. While generic LLMs can generate answers, they often lack the specialized context needed for accurate clinical coding and are prone to hallucinations. By combining LLMs with proprietary medical content, structured code sets, and editorial policies, IMO Health significantly improves AI accuracy. Haravu also explores the potential of knowledge graphs to link diagnoses, labs, medications, and procedures, as well as using AI agents to streamline dictionary migrations in EHRs.
From Interview #81
With Krish Ramadurai
Krish Ramadurai, Partner at AIX Ventures, shares insights into the most promising areas of AI-native health tech. From scalable AI diagnostics that combine multiple tests into one platform to full-stack biotech models that bypass inefficient CRO workflows, Ramadurai outlines how automation is reshaping pharma. He highlights opportunities in precision oncology, immunology, and lab automation, as well as the challenge of integrating disparate lab systems. The conversation points to a potential reinvention of big pharma, driven by efficiency gains, in-house automation, and strategic AI deployment.
From Interview #95
With Dr. Bernardo Perez-Villa
Dr. Bernardo Perez-Villa, Senior Innovations Engagement Partner at the Cleveland Clinic, shares grounded insights into the true drivers of successful innovation in healthcare. Drawing on his global background in biodesign and commercialization, he discusses the real-world challenges of bringing AI technologies from concept to clinic. From assessing unmet clinical needs to understanding payer dynamics and regulatory bottlenecks, Perez-Villa emphasizes frameworks that distinguish viable solutions from hype. Hosted by Dr. Sanjay Juneja, this conversation unpacks the realities of product-market fit, digital health business models, and how to responsibly scale innovation without compromising on patient safety or financial sustainability.
From Interview #93
With Rajeev Ronanki
Rajeev Ronanki, CEO of Lyric, explores how the FDA’s evolving stance on artificial intelligence could reshape the future of drug development and healthcare delivery. In this compelling exchange, he unpacks the promise of Elsa—a tech-forward initiative by the FDA—and why it signals a paradigm shift from bureaucratic bottlenecks to data-driven decisions. Rajeev emphasizes how AI can accelerate therapeutic innovation, reduce inefficiencies, and make regulatory processes more transparent and predictive. His insights speak directly to healthcare executives, clinicians, and digital health innovators eager to understand federal AI adoption and its implications for clinical practice and pharma R&D.
From Interview #77
With Tom Neyarapally
What’s next for AI in drug discovery and development? Tom Neyarapally, CEO of Archetype Therapeutics, shares his outlook on how converging technologies—from LLMs to spatial omics—are reshaping how we discover and deliver drugs. He emphasizes that while AI tools are evolving rapidly, the integration of diverse data modalities and collaborative innovation between nimble startups and large pharma is essential. Neyarapally also touches on reducing late-stage trial failures and costs—key challenges that AI is finally beginning to address. For stakeholders focused on AI in drug development, this conversation offers both strategic insight and practical optimism.
From Interview #82
With David Norris
David Norris, a lifelong technologist and healthcare innovator, unpacks why AI—especially large language models (LLMs)—is gaining rapid traction in healthcare. With decades of progress converging in recent breakthroughs, Norris outlines how AI can now handle tasks from reading faxes to calling patients about lab results. He emphasizes the practical LLM use cases in healthcare that free clinicians from administrative burdens, allowing more time for patient care. This transformation isn't about replacing jobs—it's about restoring the human connection in medicine by shifting repetitive tasks to AI. The conversation brings clarity to the real benefits of AI in healthcare and where it's headed next.
From Interview #87
With Rajiv Haravu
In this in-depth interview, Rajiv Haravu, SVP of Product Management at IMO Health, explores the complexities of healthcare data normalization and its vital role in improving data quality across clinical systems. Haravu outlines the challenges of variability in documentation, from lab results to unstructured physician notes, and shares how IMO’s Precision Normalize and Precision Sets products address these issues. He discusses the interplay between structured and unstructured data, the role of terminology management, and the importance of adapting to evolving definitions. With insights on AI and natural language processing, Haravu reveals how IMO is integrating advanced tools while maintaining precision in clinical coding—empowering healthcare organizations to unlock the full potential of their data.
From Interview #85
With Jason Alan Snyder
In this full interview, Jason Alan Snyder, Futurologist, Inventor, and Technologist, explores the urgent issues of AI in healthcare data, from privacy and ethics to ownership and monetization. Snyder highlights how digital twins—virtual representations of individuals built from lab results, biometrics, genomes, and behaviors—are being used without consent or compensation. He warns of the dangers of poor data quality, decay, and fragmentation, which lead to flawed AI-driven medical decisions. Snyder envisions a future where individuals own and control their health data, benefiting from AI’s potential while avoiding exploitation. The conversation offers a roadmap for building ethical, transparent, and patient-centered AI systems in healthcare.
From Interview #84
With Dr. Alister Martin
In this full interview, Dr. Alister Martin, CEO of A Healthier Democracy and Assistant Professor at Harvard Medical School, shares a powerful vision for how AI can both lower healthcare costs and expand access for underserved communities. Martin discusses the concept of 'money as medicine,' showing how targeted financial investments in social determinants of health can prevent costly emergency care. He explores AI's role in identifying high-need patients, optimizing care coordination, and connecting people to the right resources before medical crises occur. This conversation blends clinical insight with public health strategy, offering actionable ideas for leaders seeking to align cost savings with better health outcomes.
From Interview #83
With Pelu Tran
In this full interview, Pelu Tran, CEO of Ferrum Health, explores the complex realities of AI governance in healthcare and how hospitals can navigate the tension between innovation, security, and regulatory compliance. Tran discusses the technical, clinical, and business barriers to AI adoption, from vendor-cloud mistrust to the high costs of deploying and maintaining AI solutions. He explains why governance platforms are essential for ensuring AI models perform safely across diverse patient populations, and how middleware infrastructure can help hospitals integrate and manage AI at scale. This conversation offers actionable insights for leaders facing AI adoption bottlenecks.
From Interview #81
With Krish Ramadurai
In this in-depth interview, Krish Ramadurai, Partner at AIX Ventures, shares his perspective on what's real and what's overhyped in AI development in healthcare. He explains the importance of domain expertise, the role of clinical workflow automation, and why business models must align with enterprise value creation. Ramadurai also discusses AI’s role in drug development, the challenge of regulatory compliance, and the strategic investments that drive sustainable innovation. His insights provide a roadmap for healthcare leaders navigating AI investment opportunities.
From Interview #79
With Dr. Nigam Shah
Dr. Nigam Shah, Co-Founder of Atropos Health and Chief Data Scientist at Stanford Health Care, explores how AI implementation in healthcare must shift from experimental models to scalable, sustainable solutions. Drawing on analogies from automotive safety, Dr. Shah emphasizes creating ecosystems for local validation, continuous monitoring, and defining clear context for use. He discusses how AI can increase healthcare access, avoid the 'Turing trap' of replacing humans for tasks they already do, and instead focus on reducing unnecessary visits and expanding provider capacity. With insights into AI healthcare data usage and frameworks for responsible AI lifecycle management, this conversation offers a roadmap for healthcare leaders to deploy AI effectively.
From Interview #78
With Bob Battista
In this wide-ranging interview, Bob Battista explains how AI can speed drug repurposing—if data can flow across today’s regulatory and commercial silos. He points to untapped knowledge inside pharmaceutical call centers, patient-reported outcomes, and physician off‑label use that rarely reaches researchers. Battista argues that safe-harbor policies for pharmaceutical data sharing would unlock thousands of potential indications hiding in plain sight, while AI organizes mechanistic and clinical evidence for decision makers. The conversation also explores living clinical guidelines, earlier diagnostic staging, and the economics that slow evidence adoption. For healthcare leaders, the message is clear: pair modern analytics with governance reform to expand access, improve outcomes, and strengthen patient empowerment in healthcare.
From Interview #76
With Dr. Debra Patt
In this in-depth interview, Dr. Debra Patt, Executive Vice President of Texas Oncology and Chair of ASCO’s Artificial Intelligence Task Force, shares her insights on how AI is revolutionizing cancer care. She discusses real-world applications, from AI-enhanced diagnostics to ambient AI for reducing physician burnout, and explores how AI can improve patient outcomes, streamline administrative tasks, and reduce healthcare costs. Dr. Patt also highlights the promise of AI in drug discovery, clinical trial design, and personalized medicine.