2025 was the year clinical AI stopped being something you demoed and started being something you operated. Ambient scribes hit measurable adoption, structured-extraction pipelines became table stakes in the EHR, and agentic workflows graduated from "interesting" to "in production, behind a guardrail." This is what we saw, across our own work and the broader healthcare landscape, and what we are betting on for the next twelve months.
Four things that genuinely shipped
- Ambient scribes at the encounter, no longer experimental. The bar moved from "transcript with errors" to "clinically-acceptable note in one pass, with attribution."
- Structured extraction inside the EHR, pulling diagnoses, medications, and risk signals into the record, with provenance back to the source span.
- Agentic prior-auth and intake workflows, running behind human approval gates and full audit trails.
- Multi-modal triage (voice plus chat plus image) finally usable by non-technical clinicians.
Three things that broke (and got patched)
It was not all wins. The most common failure pattern we saw across teams was confident hallucination in long transcripts: a model that sounds right for 30 minutes and quietly invents a medication in minute 31. The fix was rarely a smarter model. It was a tighter feedback loop, narrower context windows, and an eval layer that watched live output against a known ground truth.
- Eval ground truth was harder than expected. Most teams under-invested in it.
- Integration debt with legacy EHRs ate more engineering time than the model itself.
- Cost discipline became real. Token spend per encounter is now a line item on operations dashboards.
What we are watching in 2026
Three shifts feel inevitable: smaller specialist models running inside the hospital perimeter, eval frameworks that look more like SRE than QA, and a slow consolidation of "AI features" into a coherent AI surface across the EHR. The teams that win the next year will be the ones who treat AI as infrastructure, not a product line.
Zowork is a healthcare and behavioral health AI engineering team. For a decade we’ve shipped clinical platforms. Now we’re building the AI that runs underneath them.