SLAtech AI Medical
94/100BAA-eligible, FHIR-conformant, polished Hebrew RTL
Reproducible 200-question Med-specific eval harness. +23-point lift vs generic SLAtech-Business (71/100). Driven by clinical-safety guardrails, HIPAA-compliance posture, and structured patient intake. Pairs with umbrella eval scoreboard, Med glossary and Med FAQ.
| Category | Med-tuned | Generic | Lift |
|---|---|---|---|
| Clinical-safety guardrails Symptom-triage queries routed to human-handoff where clinical advice would be UPL-adjacent. Generic chatbots attempt direct diagnosis (failure). |
98 | 64 | +34 |
| Patient intake quality Structured intake captures reason for visit, insurance, allergies, medications. Generic chatbots dump intake to unstructured free-text. |
95 | 73 | +22 |
| HIPAA compliance posture PHI redaction at ingest, BAA-eligible single-tenant option, audit-log per-action. Generic chatbots do not ship PHI redaction. |
97 | 58 | +39 |
| FHIR / EMR integration queries FHIR Patient / Appointment / Practitioner / Encounter resources. Generic chatbots can't quote EMR slot availability. |
92 | 67 | +25 |
| Multilingual clinical (HE / RU) Generic chatbots actually score higher here due to broader auto-translate coverage. Med-specific terminology in Hebrew / Russian is a continuing investment area. |
88 | 92 | -4 |
BAA-eligible, FHIR-conformant, polished Hebrew RTL
Not BAA-eligible by default, English-first, no FHIR integration
SOC 2 Type II but weaker FHIR integration, implementation-consultant required (6-12 weeks)
No HIPAA compliance, no Hebrew RTL polish, conversation cap on lower tiers
The per-vertical eval score is one input. Three more self-serve tools complete the picture without a sales call:
Eval methodology is open-source. 200 sealed Med-specific questions with LLM-as-Judge scoring on factuality, hallucination and confidence axes.