Clinical Quality Language (CQL) is an HL7 specification for the expression of clinical knowledge that can be used within both the Clinical Decision Support (CDS) and Clinical Quality Measurement (CQM) domains. This repository contains complementary tooling in support of that specification.

CQF Clinical Reasoning on FHIR for Java

Tooling for CQL and IG Authors

Project COLON-X (Shaping the neXt frontier in intelligent COLONoscopy)

🩺 RAGnosis — An AI-powered clinical reasoning assistant that retrieves real diagnostic notes (from MIMIC-IV-Ext-DiReCT) and generates explainable medical insights using Mistral-7B & FAISS, wrapped in a clean Gradio UI. ⚡ GPU-ready, explainable, and open-source.

This experimental application serves an open source large language model Llama-2 that is connect to a streamlit user interface. The user can define a list of custom symptoms or ICD-10 symptoms as input to the LLM. A two-step chain of prompts will output a list of differential diagnoses followed by a list of examinations to workup these diagnoses.

Fine-tuning the DeepSeek-LLM to create a medical expert for advanced clinical reasoning. Powered by Unsloth for 2x faster training and memory-efficient LoRA.

🏥 Clinical Decision Support System for Pulmonary Embolism - An agent-based AI system using LLMs and RAG to assist healthcare professionals with evidence-based PE diagnosis and treatment recommendations.

A graphical causal modeling tool tailored for physical therapy research and clinical reasoning. Part of the Clinical Inquiry Network ecosystem.

A structured clinical reasoning portfolio for medical school preparation, documenting progressive ICU case analysis across hemodynamics, shock management, ventilation strategies, neurocritical care, and trauma. All cases are hypothetical and used for premedical academic development.

A complete, clinically oriented MCAT study framework integrating high-yield science content with medical reasoning, ICU physiology, and applied problem-solving. Designed for maximal retention, clarity, and exam performance.