From Interview #87
With Rajiv Haravu
Rajiv Haravu, SVP of Product Management at IMO Health, explains how AI is being applied in the life sciences industry. He highlights use cases such as literature reviews for drug development, information extraction from billions of documents, and sentiment analysis of social media during public health events. Haravu notes that pharma companies rely on real-world data aggregated by brokers, but data quality remains a major challenge due to variability across sources. He describes how AI methods, including LLMs and supervised machine learning, are used alongside traditional techniques to normalize and extract information at scale. Key applications include automating systematic literature reviews, improving structured datasets, and identifying key opinion leaders. Haravu also emphasizes the importance of continuous model monitoring to prevent performance drift.
From Interview #94
With Emi Gal
Emi Gal, Founder and CEO of Ezra, explains how AI is transforming MRI imaging to make full-body cancer screening faster, cheaper, and more accessible. Traditional MRIs are slow and costly because they require repeated scans to reduce noise. Ezra’s FDA-cleared AI, Ezra Flash, accelerates scan time by denoising and enhancing images, cutting a typical full-body scan to 22 minutes at 9. Another AI, Ezra Assist, helps radiologists with measurements, annotations, and segmentations—reducing repetitive tasks. Finally, Ezra Reporter translates complex radiology jargon into plain English for patients. Together, these tools streamline imaging, reduce costs, and improve patient experience while maintaining safety and regulatory compliance.
From Interview #94
With Emi Gal
Emi Gal, Founder and CEO of Ezra, discusses the role of MRI in early cancer detection compared to liquid biopsies and blood tests. He explains that while liquid biopsies like Grail’s multi-cancer detection test have value, their sensitivity for early-stage cancer remains too low, with false negatives exceeding 80% in some cases. In contrast, imaging offers higher sensitivity and, when combined with liquid biopsies and protein biomarkers, creates the most effective screening protocol. Gal emphasizes that MRI allows not only early detection but also longitudinal monitoring of lesions, distinguishing benign from malignant growths. He argues that concerns about false positives are overstated, as most findings resolve through diagnostic follow-up. Ultimately, Gal envisions AI-powered MRI as a cornerstone of comprehensive cancer detection strategies.
From Interview #95
With Dr. Bernardo Perez-Villa
Dr. Bernardo Perez-Villa, Senior Innovations Engagement Partner at Cleveland Clinic, explains why so many digital health startups fail. The biggest reason is lack of validated clinical need and poor product-market fit—too many founders build solutions in search of problems. Perez-Villa emphasizes that successful startups must engage in primary and secondary market research, talking to clinicians and patients to confirm problems are real and worth solving. He also stresses that even with regulatory clearance, startups often struggle with revenue because the U.S. healthcare system is fee-for-service and reimbursement is tied to complex CPT codes, coverage, and payment structures. Without alignment on need, reimbursement, and cost, startups face what he calls the 'impossible triangle' of quality, cost, and time.
From Interview #95
With Dr. Bernardo Perez-Villa
Dr. Bernardo Perez-Villa, Senior Innovations Engagement Partner at Cleveland Clinic, clarifies the difference between disruptive and sustaining innovation in healthcare. He notes that many so-called 'first-in-class' drugs are actually incremental innovations, as Big Pharma often avoids high-risk breakthroughs, leaving startups to push boundaries until they are acquired. Drawing parallels to industries like tech and entertainment, Perez-Villa explains that truly disruptive innovations target non-consumers or overserved populations with simpler, more affordable, and more accessible offerings. He uses the iPhone and ChatGPT as examples of innovations that started clunky but rapidly improved to reshape entire markets. Perez-Villa emphasizes that in healthcare, disruptive innovation must align with business models that allow sustainability, otherwise it risks becoming just another sustaining technology.
From Interview #90
With Adrian Mendes
Adrian Mendes, CEO of Perimeter Medical Imaging, explains how OCT (Optical Coherence Tomography) technology can significantly reduce healthcare costs by lowering the number of re-excisions in breast cancer surgeries. Referencing a study from MD Anderson, Mendes highlights that each re-excision can cost ,000–,000, with mastectomies reaching up to 0,000. Beyond direct costs, repeat surgeries strain hospital operating room availability and create emotional and physical burdens for patients and providers. Mendes emphasizes that payers and insurers stand to benefit economically from adopting OCT, while patients gain from improved outcomes and reduced stress.
From Interview #90
With Adrian Mendes
Adrian Mendes, CEO of Perimeter Medical Imaging, emphasizes the critical role of high-quality data in improving surgical outcomes. He explains that while hardware and algorithms are increasingly commoditized, data remains the key differentiator for AI in healthcare. Perimeter’s OCT technology generates millions of images, which, when paired with pathology outcomes, create a robust dataset for training. Mendes highlights challenges such as variability in tissue types, definition decay, and the difficulty of obtaining de-identified yet clinically rich data. He underscores the importance of patient and hospital collaboration in contributing data to improve accuracy, reduce costs, and prevent re-operations.
From Interview #86
With Dr. Euan Ashley
Dr. Euan Ashley, Chair of the Department of Medicine at Stanford, challenges the outdated criteria for medical school admissions. He argues that while chemistry and physics have long been gatekeepers, they fail to assess skills critical for future doctors, such as AI proficiency, adaptability, and empathy. In a world where language models, multimodal diagnostics, and real-world data will be essential in care delivery, doctors unskilled in AI risk falling behind. Ashley calls for admissions reform to evaluate candidates on the abilities that will matter most in the AI-powered healthcare environment.
From Interview #77
With Tom Neyarapally
Tom Neyarapally explains how Archetype Therapeutics uses AI to revolutionize cancer research by drastically reducing drug discovery timelines. By merging established genomic classifiers—tests that predict cancer outcomes—with cutting-edge generative AI, the company can virtually screen billions of molecules in a single day. This process targets multiple genes simultaneously, identifying compounds most likely to improve patient survival. By eliminating the need to physically test low-probability candidates, this approach saves time, reduces costs, and accelerates the development of life-saving therapies.
From Interview #88
With Dr. David Fajgenbaum
Dr. David Fajgenbaum explains how Every Cure is democratizing drug discovery by releasing an open-source database of AI-generated drug-disease scores. Using advanced knowledge graphs, the platform evaluates all 4,000 approved drugs against every known disease, creating over 75 million rankings. While Every Cure uses these scores internally to prioritize research and clinical trials, they will soon make the entire dataset publicly available. This resource will allow scientists, clinicians, and even patients to identify promising drug candidates for conditions ranging from Parkinson’s to rare diseases, accelerating global collaboration in medical innovation.
From Interview #77
With Tom Neyarapally
Tom Neyarapally, CEO of Archetype Therapeutics, explains why drug discovery remains slow despite major technological advances. He highlights that while access to molecular profiling, patient clinical data, advanced AI/ML tools, and scalable compute power have transformed the field, traditional workflows remain fragmented. Neyarapally advocates for a patient-centric approach—starting with well-characterized patient cohorts and using AI to screen drugs that can move them from illness toward wellness. This method, coupled with repurposing existing safe drugs, can dramatically shorten proof-of-concept timelines.
From Interview #88
With Dr. David Fajgenbaum
Dr. David Fajgenbaum, Co-founder of Every Cure, explains how AI is accelerating the search for treatments for rare diseases through drug repurposing. By scanning all existing drugs across all diseases, AI dramatically increases the chances of finding matches for conditions that otherwise have no effective therapies. While any individual rare disease may have low odds of an existing treatment, the cumulative approach across all conditions reveals many untapped opportunities. Fajgenbaum emphasizes the moral obligation to connect patients with available therapies—especially when they already exist in local pharmacies—rather than forcing them to wait years for new drug development.
From Interview #79
With Dr. Nigam Shah
Dr. Nigam Shah, Co-founder of Atropos Health and Chief Data Scientist at Stanford Healthcare, shares his framework for assessing AI’s role in improving patient care. He describes the 'firm assessment' process, which evaluates expected benefits, feasibility, and incentives before deploying models. Shah also critiques current AI-in-healthcare research for lacking real-world EHR data and focusing solely on accuracy metrics, advocating instead for task-specific evaluation and uncertainty measures. He emphasizes that impactful AI requires starting with the right clinical question, selecting datasets fit for that question, and integrating evaluations into the organization’s responsible AI lifecycle.
From Interview #94
With Emi Gal
How can artificial intelligence make full body MRI screening faster, more affordable, and more accurate? In this compelling interview, Ezra founder and CEO Emi Gal discusses how FDA-approved AI medical devices are transforming MRI scans into a powerful tool for early cancer detection. Gal outlines Ezra's three-tiered AI pipeline—enhancing image quality, assisting radiologists, and translating complex reports into plain language—to deliver a 22-minute, 9 full body MRI scan. Learn how this innovation is helping detect cancer in asymptomatic patients and why younger populations may benefit from early, proactive screening
From Interview #85
With Jason Alan Snyder
Digital twins—AI-powered models based on individual health data—are poised to transform how healthcare predicts, treats, and personalizes care. But Jason Alan Snyder, a technologist and futurist, raises powerful ethical and technical concerns about how these systems are being deployed today. Built from EHRs, lab results, wearables, and even genomics, these digital representations can simulate decisions and even replace human judgment. Yet they are often created without consent or clarity. In this clip, Snyder urges healthcare leaders to reclaim agency over the design and governance of these digital surrogates, emphasizing that the future of AI in healthcare must be rooted in truth, transparency, and trust.
From Interview #88
With Dr. David Fajgenbaum
In this compelling conversation, Dr. David Fajgenbaum describes how his team uses a platform called Matrix, powered by a biomedical knowledge graph, to scan all known relationships between drugs, diseases, and genes. The goal? To apply AI for healthcare data analysis in identifying potential treatments, some of which had been previously overlooked or discarded due to lack of profitability. One powerful example includes the identification of a TNF inhibitor for Castleman disease that led to a lifesaving intervention. Dr. Fajgenbaum emphasizes how this technology doesn't just discover new solutions but also revalidates forgotten ones, shedding light on opportunities to repurpose existing drugs. This showcases the transformative potential of AI predictive analytics in healthcare.
From Interview #76
With Dr. Debra Patt
In this insightful interview, Dr. Debra Patt shares how AI applications in healthcare are transforming the daily realities of clinical care. As a breast medical oncologist and health policy leader, she describes the power of ambient AI scribe technology, which reduces administrative burdens and improves patient communication by instantly generating visit notes. She outlines specific benefits—from more accurate ICD-10 coding to enhanced decision-making in cancer care—that not only help providers get patients the right treatment faster, but also streamline billing and prior authorization. Her perspective offers a grounded, real-world view of "what are the applications of AI in healthcare" today, especially for oncology practices navigating high patient loads and value-based care demands.
From Interview #90
With Adrian Mendes
Adrian Mendes, CEO of Perimeter Medical Imaging, discusses how AI-powered imaging is transforming breast cancer surgery. Leveraging optical coherence tomography (OCT) combined with AI, surgeons can visualize tumor margins at microscopic levels in real-time, reducing the need for repeat surgeries. This innovation offers higher precision in tissue removal, preserving healthy tissue while ensuring complete cancer excision. Mendes also addresses broader applications in head and neck cancers and the role of high-quality data in advancing AI models. The conversation highlights economic benefits, patient psychological relief, and improved access to high-quality surgical outcomes in both urban and rural healthcare settings.
From Interview #89
With Dr. Harvey Castro
Hematologist-oncologist host Dr. Sanjay Juneja sits down with emergency physician and AI advisor Dr. Harvey Castro to demystify agentic AI in healthcare. Castro explains how clinician and patient digital twins could safely scale expertise, reduce ER bottlenecks, and improve decisions when paired with human oversight. The discussion compares build-versus-buy choices for ambient scribing and other use cases, notes when open-source models can lower costs, and outlines how federated learning lets hospitals benefit from broader data without exposing PHI. They also tackle workforce shortages, forecasting near-term wins from ambient documentation, monitoring at home, and culturally aware guidance at the bedside. Throughout, Castro stresses pragmatic guardrails—HIPAA, FDA pathways, and clinician validation—to mitigate hallucinations and bias. For leaders planning an AI strategy, the conversation offers a clear, reader-first roadmap that highlights ai agents in healthcare while staying focused on patient outcomes.
From Interview #88
With Dr. David Fajgenbaum
In this insightful interview, Dr. David Fajgenbaum, Co-founder of Every Cure, shares how artificial intelligence is revolutionizing rare disease research and treatment. Drawing from his own groundbreaking work in drug repurposing and precision medicine, Dr. Fajgenbaum explains how AI tools can rapidly identify therapeutic opportunities hidden in existing medical data. He discusses the challenges of rare disease diagnosis, the importance of cross-disease data analysis, and the potential of AI to accelerate life-saving discoveries. Through a mix of personal experience and scientific expertise, he paints a compelling vision for how AI could reshape the landscape of rare disease care and improve patient outcomes worldwide.
From Interview #86
With Dr. Euan Ashley
In this full interview, Dr. Euan Ashley, Chair of the Department of Medicine at Stanford, shares how preventive cardiology is being transformed by remote monitoring, AI, and wearable technologies. Ashley highlights how early detection through wearable devices can identify subtle changes in cardiac function before symptoms appear, enabling timely interventions that prevent severe events. He discusses advances in remote cardiac monitoring, AI-powered risk prediction, and how continuous data streams are improving both individualized care and population health. This conversation offers a compelling vision for integrating cutting-edge technology into preventive cardiovascular medicine.
From Interview #80
With Dr. Azra Raza
Dr. Azra Raza, Professor of Medicine at Columbia University, challenges the cancer research community to shift focus from treating late-stage disease to detecting cancer at the 'first cell' stage. Drawing on decades of clinical work and a unique repository of 60,000 patient samples, she outlines how AI-powered, continuous monitoring could revolutionize early detection. Dr. Raza critiques the limitations of current models and screening methods, advocating for human-based research and proactive monitoring to prevent cancer before it requires invasive treatment. Her vision combines multi-omics analysis with implantable biosensors to identify biomarkers long before cancer reaches stage one, aiming to reduce suffering and improve outcomes.
From Interview #77
With Tom Neyarapally
In this full interview, Tom Neyarapally, CEO and Co-Founder of Archetype Therapeutics, discusses the transformative role of AI in drug development. He explores how generative AI is accelerating drug discovery, enabling drug repurposing, and reshaping therapeutic innovation. Neyarapally provides a forward-looking perspective on the future of AI in healthcare, drawing from his deep expertise and hands-on leadership in the biotech industry.