AI-native architecture built for clinical workflows, not retrofitted from enterprise software
Infrastructure designed from the start for AI-assisted clinical documentation
ClinixSummary uses current-generation LLMs with prompt engineering optimized for clinical documentation. We're evaluating fine-tuned medical models for future versions to improve specialty-specific accuracy.
Sub-second response times for transcription and note generation. Optimized inference pipelines ensure documentation keeps pace with clinical conversation flow.
Designed for GDPR and HIPAA-aligned deployments. Encryption in transit and at rest, data minimization principles, and configurable retention policies. No patient data used for model training without explicit consent.
API-first design for EHR integration. Current export options include copy/paste, email, and file download. Push integrations to major EHR systems are on the roadmap.
Scalable cloud deployment with regional data residency options. Architecture supports single clinicians to multi-site healthcare organizations with the same codebase.
Native iOS and Android applications built for clinical environments. Optimized for reliability in varied network conditions, quick launch times, and minimal battery impact.
How we measure performance and ensure responsible AI deployment
We measure AI output quality against clinician-edited final documentation. Key metrics include:
Complete traceability from input to output for quality assurance and incident investigation:
ClinixSummary is a documentation assistant, not an autonomous clinical decision system. The design principle is explicit:
AI generates draft documentation from clinical input
Clinician reviews, edits, and approves final note
Final clinical decisions remain with the healthcare professional
From documentation assistance toward deeper clinical integration-with appropriate safety validation at each stage
Current: AI-assisted note generation, ICD-10 suggestions, multilingual support. Clinician reviews all output.
Next: EHR push integration, patient timeline aggregation, cross-encounter pattern recognition for individual patients.
Future: Validated clinical decision support tools, pending regulatory pathway completion and clinical validation studies.
See the technology in action with ClinixSummary, or discuss technical requirements for your organization.