Tagged: healthcare AI
6 articles on healthcare ai.
Healthcare AI Will Be Won by Verticals. The Recipe Has Been Around for a Decade.
Most healthcare AI companies are failing for the same reason. The ones winning are all running the same playbook — one that Palantir figured out long before anyone called it AI. Forward-deployed engineer. Ontology. Integrations. Then AI tooling, and only then.
Vision + Language: How Multimodal LLMs Actually Work (And When to Use Them)
Multimodal LLMs integrate vision through two fundamentally different architectures. Knowing which one you need — and why — is the decision that shapes every other technical choice in your build.
FHIR Meets Graph Databases: Exploring Healthcare's Natural Network Structure
How FHIR's interconnected resources transform into powerful graph relationships. Exploring the potential of graph technologies in healthcare AI at Clarity Health Project.
The GenAI Strategy Question You're Not Asking (But Should Be)
Everyone asks 'how should we use GenAI?' The honest answer requires a harder question first: does AI's unique capability actually create new value here, or is it just a more expensive way to do something that already worked? A practitioner's framework for getting this right — especially in healthcare.
Inside the Black Box: What Mechanistic Interpretability Means for Builders
Healthcare AI requires explainability — "the model said so" is not a clinical rationale. Mechanistic interpretability is the research field trying to change that. Here is what it actually offers practitioners today, what the gap still is, and what you can do in the meantime.
How to Actually Test If Your AI Will Say Something Dangerous
Most teams treat jailbreak testing as a vibe check. StrongREJECT achieves 0.90 Spearman correlation with human judgment — which means automated safety evaluation is real, and there is no good excuse not to build it into your pipeline.