Introducing Open Book Medical AI: Deterministic Knowledge Graph + Compact LLM Most medical AI systems today rely heavily on large, opaque language models. They are powerful, but probabilistic, difficult to audit, and expensive to deploy. We’ve taken a different approach. Our medical AI is a hybrid system combining: • A compact ~3GB language model • A deterministic proprietary medical Knowledge Graph (5K nodes, 25K edges) • A structured RAG-based answer audit layer The Knowledge Graph spans 7 core medical categories: Diseases, Symptoms, Treatment Methods, Risk Factors, Diagnostic Tools, Body Parts, and Cellular Structures and, critically, their relationships. Why this architecture matters 1️⃣ Comparable answer quality with dramatically lower compute and reduced hallucination. A ~3GB model can run on commodity or on-prem infrastructure, enabling hospital deployment without the heavy cloud dependency typically associated with 80GB-class LLMs. 2️⃣ Deterministic medical backbone The Knowledge Graph constrains reasoning. No hallucinated treatments. No unsupported disease relationships. Medical claims must exist within structured ontology. 3️⃣ Verifiable answers via RAG audit Every response can be traced back to specific nodes and relationships in the graph. Symptom → Disease → Diagnostic Tool → Treatment. Structured, auditable, explainable. 4️⃣ Separation of language from medical truth The LLM explains and contextualizes. The Knowledge Graph validates and grounds. This architectural separation dramatically improves reliability and regulatory defensibility. 5️⃣ Complete control over the core of truth Unlike black-box systems that rely entirely on opaque model weights, this architecture gives full control over the medical knowledge layer. You decide what is included, how relationships are defined, and how updates are governed. In high-stakes domains like healthcare, scaling parameter count is not the only path forward. Controllability, traceability, and verifiability may matter more. Hybrid architectures that combine probabilistic language models with deterministic knowledge systems offer a compelling alternative. The model is capable of clinical case analysis and diagnostic reasoning. It is currently available for public testing on Hugging Face Spaces (shared environment, typical response time: 15–30 seconds): https://huggingface.co/spaces/cmtopbas/medical-slm-testing Happy to connect with others exploring Knowledge Graph + LLM systems in regulated domains. #MedicalAI #HealthcareInnovation #KnowledgeGraphs #ExplainableAI #RAG #ClinicalAI #HealthTech submitted by /u/vagobond45
Originally posted by u/vagobond45 on r/ArtificialInteligence