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by TensorBlack
AI and Healthcare by TensorBlack is a clinician-led conversation about what artificial intelligence is actually doing in cancer care — and what it will take to deploy it, and to trust it. Sanjay Juneja, MD, is a triple board-certified hematologist and medical oncologist and VP of Clinical AI at Tempus AI. Douglas Flora, MD, LSSBB, is executive medical director of oncology services at St. Elizabeth Healthcare and editor-in-chief of the journal AI in Precision Oncology. Guests are among the most credible voices in oncology and AI: Eric Lefkofsky, founder and CEO of Tempus AI; Anant Madabhushi, PhD, of Emory University; and Scott Penberthy of Google — alongside cancer center executives, health-system leaders, health-policy attorneys, and the researchers publishing the underlying work. Conversations cover computational pathology and radiology, precision medicine and genomic sequencing, clinical decision support, digital twins, reimbursement and governance, drug discovery, and where the technology still falls short. Every episode works toward the same question: what does AI change in the clinic on Monday? Made for oncologists, cancer center and health system leaders, and the people deciding what to build, buy, validate, and deploy. The show launched in 2021 as the TARGET: Cancer Podcast, hosted by Sanjay Juneja, MD and Mika Newton, and became AI and Healthcare after 75 episodes.
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- 2 days ago47 min
Can a Mammogram Predict 5-Year Breast Cancer Risk Using AI?
Can a routine mammogram tell a woman her five-year breast cancer risk? Connie Lehman, MD, PhD spent her career in breast imaging and built an AI model that reads risk straight from the mammogram, with no questionnaire. In this episode of AI and Healthcare, oncologists Sanjay Juneja, MD and Douglas Flora, MD talk with Lehman, founder and CEO of Clairity, about breast cancer screening, breast cancer risk assessment and what changes when the mammogram carries the risk score. 📩 Sign up for The Signal by TensorBlack, our newsletter on AI and cancer care and what it means for your practice. Free, every week: https://tensorblack.beehiiv.com/subscribe
- 2 weeks ago46 min
How AI Reads Blood Cells to Help Diagnose Cancer with Gregory Goldgof, MD, PhD
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.
- Oct 13, 202643 min
What Changes When AI Can Read All of the Electronic Health Record?
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 <
- Sep 29, 202639 min
The AI Doctor Debate: Autonomous AI in Healthcare with Ezekiel Emanuel, MD, PhD
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.
- Sep 3, 202655 min
What AI Can See in Medical Scans That Doctors Can't
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.
- Aug 14, 202653 min
This Oncologist Started Learning AI at 60, Here's How He Uses It
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.
- Jul 27, 202640 min
Can CMS turn Healthcare AI into a Billable Service
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.
- Jul 10, 202656 min
The End of Trial-and-Error Cancer Care
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.
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About AI and Healthcare
AI and Healthcare by TensorBlack is a clinician-led conversation about what artificial intelligence is actually doing in cancer care — and what it will take to deploy it, and to trust it. Sanjay Juneja, MD, is a triple board-certified hematologist and medical oncologist and VP of Clinical AI at Tempus AI. Douglas Flora, MD, LSSBB, is executive medical director of oncology services at St. Elizabeth Healthcare and editor-in-chief of the journal AI in Precision Oncology. Guests are among the most credible voices in oncology and AI: Eric Lefkofsky, founder and CEO of Tempus AI; Anant Madabhushi, PhD, of Emory University; and Scott Penberthy of Google — alongside cancer center executives, health-system leaders, health-policy attorneys, and the researchers publishing the underlying work. Conversations cover computational pathology and radiology, precision medicine and genomic sequencing, clinical decision support, digital twins, reimbursement and governance, drug discovery, and where the technology still falls short. Every episode works toward the same question: what does AI change in the clinic on Monday? Made for oncologists, cancer center and health system leaders, and the people deciding what to build, buy, validate, and deploy. The show launched in 2021 as the TARGET: Cancer Podcast, hosted by Sanjay Juneja, MD and Mika Newton, and became AI and Healthcare after 75 episodes.
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