Why calibrated uncertainty is more important than raw accuracy in medical AI
An AI that says "I'm 97% confident" and is wrong 20% of the time is more dangerous than one that says "I'm 72% confident" and is right 72% of the time. Here's how we approach uncertainty in ClinexaOS and why we think the industry's obsession with top-line accuracy is misplaced.
Read ArticleWhat radiologists actually want from AI pre-reads (and what vendors keep getting wrong)
We surveyed 140 radiologists across 8 institutions. The results challenged several of our assumptions.
Publishing negative results: why we released a study where our model underperformed
Transparency in AI means publishing failures as prominently as successes. Here's a case where we fell short and what we learned.
Zero-persistence architecture: how ClinexaOS ensures no patient data survives a session
A technical deep-dive into our data handling architecture and the engineering choices that make it genuinely private.
Deploying diagnostic AI in low-resource settings: lessons from three months in East Africa
What works, what doesn't, and why most enterprise AI tools fail before they even get started in underserved contexts.
Specialty-context injection: how we teach a single model to think like a neurologist or a cardiologist
The architecture behind our multi-specialty inference system — and why we chose it over separate specialty models.
Structured reports that clinicians actually read: our design process for diagnostic output formatting
Good AI output is not just accurate — it's legible, actionable, and structured for the workflow it's entering.