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In this episode of AI and Healthcare, oncologists Sanjay Juneja, MD and Doug Flora, MD sit down with Greg Goldgof, MD, PhD, MS, a computer scientist and board-certified pathologist, to talk about DeepHeme, the AI system his lab built to classify the cells in a bone marrow aspirate, the test at the center of diagnosing leukemia and other blood cancers, and what it took to get it validated and into clinical use. Gregory Goldgof, MD, PhD, MS is Director, Artificial Intelligence, Hematopathology Service, Memorial Sloan Kettering Cancer Center. He is a computer scientist and a pathologist board-certified in clinical pathology and clinical informatics. His lab builds AI tools for hematopathology, hematology and cytology.

Every oncologist knows the electronic health record is where the cancer story goes to hide. Outside records arrive by fax, get scanned into the media tab under the wrong name, and pile up to hundreds of documents per patient from dozens of institutions, written in coding languages that never quite translate. Screening one chart for a clinical trial takes an hour. Tumor registries run months behind. Drugs get denied because a performance status was in the note but not in the request. No wonder "data" can feel like a four-letter word. This episode is about what changes when AI can finally read all of it.   In this episode of AI and Healthcare, oncologists Sanjay Juneja, MD and Doug Flora, MD sit down with Mika Newton, CEO of xCures, to unpack a new AI in Precision Oncology paper on using large language models to turn hundreds of medical records per cancer patient into structured, verifiable healthcare data.   In this episode: • Why a cancer patient's medical records can run to 231 documents from dozens of institutions, and why most of it is still not in discrete fields • The many "languages" of healthcare data (OMOP, SNOMED, ICD, CPT) and why the crosswalks are never one-to-one • Learning the structure of a document before extracting from it, and why 95% of the work is organizing rows and columns • The three buckets: what to automate, where a human belongs in the loop, and when to give up on an unreadable page • How to vet a data vendor: dual human extraction, model drift, a 17-page buyer's guide, and whether it pays for itself • Interoperability in healthcare: TEFCA, Individual Access Services, CMS's Kill the Clipboard program, and what changes when patients own their own health data • The disparity question nobody has solved yet   Mika Newton is Chief Executive Officer of xCures, which has applied AI to organizing and extracting structured data from medical records since 2018. He is a founder of this podcast and a member of the editorial board of AI in Precision Oncology, the journal publishing the paper discussed in this episode. Doug Flora, MD is the journal's Editor-in-Chief; the paper went through independent peer review.   Read the paper: https://journals.sagepub.com/doi/10.1177/2993091X261492372 xCures Buyers Guide: https://xcures.com/interactive-buyers-guide/ Kill the Clipboard: https://www.cms.gov/health-tech-ecosystem/early-adopters/kill-the-clipboard   Subscribe for conversations about artificial intelligence, oncology, clinical medicine, and the future of healthcare.   Hosts: Sanjay Juneja, MD: https://www.linkedin.com/in/sanjayjunejamd Douglas Flora, MD: https://www.linkedin.com/in/douglasflora   Follow TensorBlack on X: https://x.com/tensorblack Follow TensorBlack on Instagram: https://www.instagram.com/tensorblack Follow TensorBlack on LinkedIn: https://www.linkedin.com/company/tensorblack/ Follow TensorBlack on Facebook: https://www.facebook.com/tensorblack   00:00 AI and Healthcare 00:30 Introduction 01:59 COI 03:01 AI and clinical documentation 04:40 Medicine runs on documents 06:04 OMOP, SNOMED, ICD, CPT: healthcare data languages 08:52 Fax machines and HIPAA 10:04 The media tab problem 11:12 How xCures started 12:03 Document structure before extraction 14:46 231 documents per cancer patient 16:19 Data science is 95% data prep 17:07 Clinical trial screening: an hour per chart 19:14 LLM extraction: enrichment and inference 20:48 AI and healthcare labor 21:58 Human in the loop: the three buckets 24:13 Why the journal published the paper 26:09 AI hallucinations: measure, don't worry 27:17 How to vet a healthcare data vendor 28:05 Dual extraction, model drift, buyer's guide 30:39 Healthcare AI ROI 31:50 EHR integrations vs. patient apps 32:31 Healthcare interoperability, solved in 2023 33:34 TEFCA, Individual Access Services, Kill the Clipboard 35:17 Patient-owned health data 36:00 Health records outside HIPAA 38:22 Digital pathology and health disparities 40:56 Every medical record is a patient 42:47 Data that changes a patient's life   #AIinHealthcare #MedicalRecords #CancerCare

Will AI replace doctors? Ezekiel "Zeke" Emanuel, MD, PhD, thinks autonomous AI will soon manage a lot of what physicians do today, starting with chronic disease and the side effects of chemotherapy, and that the better it gets, the more likely a human override is to add errors rather than catch them. His recent JAMA papers made the argument in print. In this episode of AI and Healthcare, oncologist Sanjay Juneja, MD sits down with Emanuel, an oncologist, bioethicist and Penn's Vice Provost for Global Initiatives, to talk through where autonomous AI in healthcare arrives first, where it will fail, and what is left for physicians when it does.

What if medical scans we already collect contain signals that could predict cancer treatment response, reveal disease risk years earlier, or identify biological patterns the human eye cannot see? In this episode of AI & Healthcare, oncologists Dr. Sanjay Juneja and Dr. Doug Flora, speak with AI and biomedical imaging researcher Anant Madabhushi, PhD about the hidden information inside routine CT scans, retinal images, and pathology slides. They explore how AI could help predict treatment response, detect warning signs of disease, uncover meaningful signals in tumor vessels and surrounding tissue, and expand access in regions facing shortages of radiologists and pathologists. The conversation also examines explainability, automation bias, clinical validation, health equity, and the consequences of waiting too long to use potentially valuable technology. Anant Madabhushi, PhD is the Robert W. Woodruff Professor of Biomedical Engineering at Emory University and Georgia Tech and Executive Director of the Emory Empathetic AI for Health Institute. His work focuses on AI, radiomics, computational pathology, and predicting treatment response from routinely collected medical images. Subscribe for conversations about artificial intelligence, oncology, clinical medicine, and the future of healthcare.

Medicine is becoming too complex for any one clinician to keep up with alone. AI may soon change that equation. Dr. Francisco Esteva joins Dr. Sanjay Juneja and Dr. Doug Flora to explain how physicians can use AI to research faster, communicate more clearly with patients, automate repetitive work, and build systems that extend their expertise. Francisco began learning these tools around age 60. Today, he's building AI-powered workflows for patient education, YouTube, research, quality control, and automation. You don't have to become an AI expert. But understanding how to use these tools may soon become an essential clinical skill.

CMS just proposed rules that could decide who gets paid for AI in healthcare, and that's a bigger deal than another “better algorithm” story. Dr. Sanjay Juneja and Dr. Douglas Flora talk with Jordan Johnson (radiation oncology, healthcare finance, compliance and law) about what's actually in the proposal and what happens once it lands. Topics: when does AI actually count as a billable clinical service. Why getting paid also means getting watched more closely. Hospitals leaning toward integrated platforms over one-off point solutions. What to do now, before the rule locks in. Still just a proposal, not law yet, so we should say that somewhere.

Eric Lefkofsky, Founder and CEO of Tempus AI, joins Dr. Sanjay Juneja and Dr. Doug Flora to explore how artificial intelligence, real-world data, genomic sequencing, and emerging technologies like organoids are reshaping oncology. From reducing diagnostic errors to matching patients with more effective therapies, AI is transforming how clinicians make decisions and how researchers develop the next generation of treatments. The conversation examines why healthcare adoption has lagged behind technological progress, the growing role of patient-driven intelligence, and how connected data may help eliminate thousands of preventable cancer deaths each year. Eric also shares his vision for AI-powered clinical decision support, digital twins, drug development, and the future relationship between physicians and intelligent systems.

What happens when artificial intelligence becomes a trusted partner in patient care? Healthcare leaders are increasingly exploring how AI can help clinicians make faster, more informed decisions while reducing administrative burden and improving patient outcomes. But deploying AI in medicine requires balancing innovation, trust, regulation, and clinical judgment. In this conversation, Dr. Scott Penberthy, Dr. Lindsey Cotton, and Dr. Doug Flora explore the future of cancer care, precision medicine, digital twins, physician adoption, implementation science, and the role of AI in supporting—not replacing—clinical decision-making. From cancer screening and treatment selection to workflow automation and predictive modeling, they discuss what healthcare may look like when every clinician has access to intelligence that helps them see what was previously invisible.

Cancer is often most treatable when it's found early—but what if we could detect it before symptoms ever appear? Advances in genomics, blood-based biomarkers, and artificial intelligence are giving researchers new ways to identify cancer earlier, understand disease at a deeper level, and move beyond one-size-fits-all treatment approaches. In this conversation, Dr. Scott Penberthy, Dr. Lindsey Cotton, and Dr. Doug Flora explore how emerging technologies are reshaping cancer detection, precision medicine, drug discovery, and clinical decision-making. They discuss the growing role of multimodal AI, the promise of next-generation sequencing, and why the future of healthcare may depend on finding patterns humans simply cannot see on their own. The goal isn't just better technology—it's earlier intervention, more personalized care, and better outcomes for patients.

Cancer treatment may be missing one of the body’s most important biological signals: circadian rhythm. In this conversation, sleep neurologist Dr. Anne Marie Morse explains how sleep timing, inflammation, immune function, and wearable data could fundamentally reshape cancer care. Emerging research suggests the timing of immunotherapy, chemotherapy, and even sleep disruption itself may influence treatment response, survival, and recurrence risk. The discussion explores chronotherapy, circadian genes, microbiome interactions, and how AI-driven analytics may help clinicians move beyond episodic care toward continuous, personalized monitoring.

AI is transforming healthcare at an unprecedented pace—but the biggest risks aren’t clinical, they’re legal, operational, and systemic. From data center infrastructure and environmental impact to reimbursement shifts and agentic AI workflows, the stakes are rising fast. New models from major tech players are poised to influence care delivery—and even get reimbursed based on outcomes—raising critical questions about ownership, liability, and control. Where does responsibility fall when AI drives decisions? How should hospitals evaluate vendors, data sources, and infrastructure risks? And what happens when automation outpaces regulation?

AI is making remarkable progress in healthcare — but translating that progress into real clinical impact is more complex than it seems. In this episode, we break down the State of Clinical AI in 2026, separating signal from noise and exploring what’s actually working today.

AI is exploding in healthcare — but most startups still fail. In this episode of the AI & Healthcare Podcast, Dr. Sanjay Juneja speaks with innovation leaders Dr. Bernardo Perez-Villa and Dr. Peter Alperin about the reality of building successful medical technology in 2026. Hundreds of AI tools have FDA clearance, billions have been invested in digital health, and yet hospitals still struggle to adopt new technology. Why?

Care is delivered every day in American hospitals, but getting paid for that care has become a battle. Denials, prior authorization, billing complexity, and rising costs have created a financial crisis behind the scenes of modern medicine. Insurance companies use automation to control payments, while hospitals struggle to keep up. Now AI is entering the fight. Dr. Sanjay Juneja and Dr. Douglas Flora speak with Monique Lappas, Founder and CEO of Qualify Health, about the growing war over who pays for care and how intelligent automation is helping providers recover lost revenue, reduce patient debt, and keep hospitals from closing.

Mental health is now central to high-quality cancer care, yet access, diagnostic precision, and scalability remain major gaps. In this in-depth discussion, clinical and innovation leaders explore how artificial intelligence can shift mental health care from reactive support to precision-driven, proactive intervention. The conversation examines AI as a clinical signal amplifier that enhances human judgment without replacing it. Topics include objective diagnostics, functional brain mapping, digital phenotyping, therapeutic bots, longitudinal monitoring, and how AI can better match patients to the right level of care at the right time. The discussion also addresses real-world implementation challenges in oncology and health systems, including workforce shortages, equity, and clinical oversight.