Evidence-Based Medical AI
Eleven specialised models across thirteen services, tuned for Egyptian Arabic and grounded in published clinical evidence.
Multi-Stage Clinical Intelligence Engine
Medical RAG (Retrieval-Augmented Generation)
General-purpose language models invent medical facts. CareFlow constrains its reasoning to retrieved evidence: WHO clinical guidelines, NICE guidance and standard reference texts including Harrison's, Kumar & Clark, Davidson's, Macleod's and the Oxford Textbook of Medicine. Retrieval is hybrid, and a cross-encoder re-ranks results before they reach the reasoning step. Every suggestion carries the evidence it rests on, so you can check it rather than trust it.
Human-in-the-Loop Safety Safeguards
CareFlow does not prescribe, diagnose, or act autonomously. It functions exclusively as a physician assistant. The licensed healthcare provider retains complete control to review, edit, approve, or reject all AI outputs.
PII Masking Before Inference
Uploaded lab documents pass through a masking pipeline that detects and blacks out patient-identifying regions before the image reaches any third-party model. Our data handling is designed around Egypt's Personal Data Protection Law (Law 151/2020).
Multi-Modal Diagnostic Fusion
Combines the Arabic voice history, structured laboratory values and the uploaded radiology image into one clinical context, then correlates across all three to produce ranked differentials with the supporting, contradicting and still-missing evidence for each.
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Join forward-thinking clinics using CareFlow to eliminate clinical documentation burden, accelerate diagnostic intake, and deliver evidence-backed care.