Latest posts
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What Should Be the Response for “Doctors Who Code” and Humanity’s Next Medical Exam
In their NEJM AI editorial, Gallifant and Bitterman remind us that only 40% of a physician’s shift is spent in direct patient contact. The rest — the invisible 60% — disappears into bureaucracy, fragmented data, coordination loops, and the friction of misaligned incentives. For those of us building or coding in medicine, that statistic should…
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🎙️ Voice, Vision, and the Doctor in the Loop
Progress by Abstraction series for Doctors Who Code Part 3 The Next Abstraction in Healthcare “At the highest level of abstraction are visual and audio interfaces… the demarcation between technical and non-technical professionals is breaking down.” — Sean McClure, Discovered Not Designed 🌍 The Rise of Invisible Interfaces There was a time when the frontier…
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🧠 From Machine Code to Medical Code
Progress by Abstraction series for Doctors Who Code Part 2 How Abstraction Shapes Clinical Reasoning and AI in Healthcare “Each generation begins not from zero, but from the highest abstraction level achieved by the previous one.” — Sean McClure, Discovered Not Designed ⚙️ The Evolution of Code — From Binary to Meaning In the beginning,…
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🩺 The Bridge Builder’s Mind: Progress by Abstraction
Progress by Abstraction series for Doctors Who Code. Part 1: “Human ingenuity is bootstrapped.” — Sean McClure, Discovered Not Designed Modern medicine and modern technology share a common secret: neither advances because people get smarter. We advance because we build smarter layers — scaffolds that make the next leap possible. This is the story of…
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🧩 Progress by Abstraction: How Doctors Build the Future of Thought
Introducing a New Series on DoctorsWhoCode.blog 🌍 Discovering a New Lens on Human Progress Every so often, a book reshapes how you see not just technology — but the human story itself.For me, Sean McClure’s Discovered, Not Designed has done exactly that. McClure is a systems thinker and technologist who argues that our greatest advancements…
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📊 Scalars and Vectors: The Building Blocks of Machine Learning in Medicine
IntroductionWhen clinicians hear about artificial intelligence, we often jump straight to buzzwords like “deep learning” or “neural networks.” But behind every powerful AI tool — from imaging diagnostics to predictive models in obstetrics — lies simple math. In Why Machines Learn, Anil Ananthaswamy begins by highlighting scalars and vectors as the foundation of machine learning.…
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Data-Driven Pregnancies: Continuous Glucose Monitoring and the Revolution in Diabetes Care
Data-Driven Pregnancies: Continuous Glucose Monitoring and the Revolution in Diabetes Care By Chukwuma Onyeije, MD, FACOG For medical professionals exploring how technology can transform patient outcomes, the management of diabetes in pregnancy (DIP)—whether Type 1 Diabetes (T1D), Type 2 Diabetes (T2D), or Gestational Diabetes Mellitus (GDM)—is a powerful case study. The stakes could not be…
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What Is Machine Learning?
Reflections from reading Anil Ananthaswamy’s Why Machines Learn Deep Dive Podcast: 📖 Introduction I’ve recently started reading Why Machines Learn by Anil Ananthaswamy, a book that traces the elegant mathematics behind modern artificial intelligence. As I work through the chapters, I want to practice “reading in public” — sharing insights while the ideas are still…
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When Fear Clouds the Future: Why AI in Healthcare Deserves Nuance, Not Alarm Bells
“AI said it was fine, then a human died.” That’s the headline that sticks — dramatic, chilling, and meant to slam the brakes on any optimism about AI in medicine. But here’s the truth: fear-driven narratives, while gripping, obscure the very real progress AI is already making in healthcare. At Doctors Who Code, we live…
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Monte Carlo Simulations in Maternal-Fetal Medicine: A Computational Approach to Clinical Uncertainty
Transforming clinical variability into actionable probabilities for evidence-based maternal-fetal care Introduction: Harnessing Uncertainty in Medicine Precision is the aspiration of medicine, yet uncertainty shapes every decision. In maternal-fetal medicine (MFM), this uncertainty compounds—we are caring for two patients at once, with overlapping but not identical risks. Should we induce at 37 weeks to protect the…