Unstructured clinical data
Patient records, discharge summaries, and imaging reports contain critical information locked in unstructured formats that standard analytics tools cannot process.
ARCHITECTURE INPUT
INDUSTRY / HEALTHCAREOPERATING CONTEXT
AI for clinical intelligence and diagnostic support.
OSYSTIC builds AI systems for healthcare teams — from medical imaging pipelines to clinical NLP and patient risk models deployed inside your existing infrastructure.
HealthcareCONTEXT MODEL
CLINICAL CONTEXTHUMAN REVIEW
EHRRECORDS
PACSIMAGING
NLPEXTRACT
REVIEWCLINICIAN
EHRPRIVACYREVIEWINTEROP
CONSTRAINT MAP
What changes in healthcare.
Healthcare data is rich, sensitive, and underutilised. We help clinical and operational teams build AI systems for regulated environments, with human review, data handling, and validation requirements defined around the clinical workflow.
Regulatory and privacy constraints
Privacy, security, data residency, and applicable regulatory obligations need to be treated as architecture inputs rather than retrofitted late in delivery.
ARCHITECTURE INPUT
Integration with legacy systems
Most healthcare organisations run EHRs and PACS systems that are decades old. New AI tools must integrate without replacing the clinical workflow.
ARCHITECTURE INPUT
SYSTEM PATTERNS
Engineering around the workflow.
These are capability patterns, not guaranteed outcomes. Exact design depends on the client’s data, infrastructure, risk profile, and validation requirements.
01
Medical imaging analysis
Computer vision models for radiology, pathology, and dermatology — trained on your annotated data and deployed inside your existing PACS environment.
02
Clinical NLP
Named entity recognition, ICD coding assistance, and discharge summary analysis that extracts structured data from clinical free text.
03
Patient risk stratification
Machine learning models that identify high-risk patients using EHR data, enabling earlier intervention and better resource allocation.
04
Drug discovery pipelines
Molecular property prediction and screening models that accelerate early-stage research workflows.
05
On-premise deployment
Where required, models and supporting services can be designed for controlled or on-premise deployment. Data movement and processing boundaries are defined explicitly for the engagement.
OSYSTIC / PROJECT CHANNELOPEN
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Building for healthcare?
Bring the real workflow, data boundary, infrastructure, and risk constraints. We will map the engineering path around them.