Andychen

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

FHIR data sits at the center of every clinical AI project I ship. Not because it’s trendy, because it gives me a reliable, inspectable substrate for models that need to survive audits, PHI controls, and cranky integration tests. If you’ve ever wrestled free‑text notes into features (and watched your evaluation crumble under edge cases), you

If you’re scanning for medical AI trends 2025 and need more than hype, here’s my working map from the past year of prototyping LLMs and vision models in HIPAA-eligible stacks. I’ll keep it practical, benchmarks I observed in controlled experiments, failure modes noted in pilot studies, and the guardrails I actually use when integrating into