medical

When I first tested BiomedCLIP on a mixed set of de-identified chest X‑rays and pathology slides from our internal sandbox, I wasn’t looking for a flashy demo. I wanted to know one thing: can this model reliably line up real clinical images with expert-level text under the constraints of HIPAA/GDPR and hospital IT? BiomedCLIP, released […]

When I evaluate a medical LLM for a real deployment, triage chatbot, decision-support tool, or internal research assistant, I’m not looking for cool demos. I’m looking for evidence: training data, benchmarks, reproducible scripts, and a clear risk profile. Clinical Camel and PMC-LLaMA are two of the most important open-source medical LLM families right now. I’ve

When teams ask me about Epic AI scribe integration, they’re rarely looking for another glossy demo. They want to know: Can I ship this into production under HIPAA, keep clinicians safe from hallucinations, and actually reduce burnout without wrecking our change‑control process? In this text, I’ll walk through how I approach Epic-integrated AI scribes from

When I’m asked whether a medical LLM is “ready for production,” I never answer with a single metric or leaderboard rank. In regulated care settings, I care about one thing: how the model behaves inside real clinical workflows under worst‑case conditions. That’s where the MedHELM framework comes in. Building on Stanford’s HELM initiative, MedHELM gives

When I first ran MedSigLIP on a batch of de‑identified chest X‑rays, what struck me wasn’t raw accuracy, it was how quickly I could prototype a clinically sensible triage system without a single task-specific label. If you’re working in a HIPAA/GDPR‑bound environment, your bar is probably the same as mine: transparent benchmarks, reproducible code, and

When I first tested LLaVA-Med on de-identified chest X-rays from a teaching PACS, I wasn’t looking for magic, I was looking for failure modes. Could a one-day–trained multimodal model from Microsoft Research handle real clinical phrasing, non-perfect images, and ambiguous findings without hallucinating itself into medicolegal trouble? In this tutorial, I walk through how I

When I ship a medical AI system into a regulated environment, the single biggest predictor of downstream pain isn’t the model architecture, it’s the dataset. Whether I’m validating hallucination rates in an LLM-based CDS tool or stress-testing a sepsis model against edge cases, the choice of medical AI datasets determines what I can credibly claim

Disclaimer: The content on this website is for informational and educational purposes only and is intended to help readers understand AI technologies used in healthcare settings. It does not provide medical advice, diagnosis, treatment, or clinical guidance. Any medical decisions must be made by qualified healthcare professionals. AI models, tools, or workflows described here are

Disclaimer: The content on this website is for informational and educational purposes only and is intended to help readers understand AI technologies used in healthcare settings. It does not provide medical advice, diagnosis, treatment, or clinical guidance. Any medical decisions must be made by qualified healthcare professionals. AI models, tools, or workflows described here are

⚠️ WARNING: This post reflects only the author’s individual, unvalidated practices in research/prototype environments. None of the methods have prospective clinical validation, IRB approval, or regulatory clearance (FDA/CE/NMPA etc.). Do NOT use any technique described here in real patient care or regulatory submissions without independent validation and approval. Explainable AI isn’t a feel‑good add‑on in