@razeen_ahmed172

Project Overview: Dr. ROCm is an advanced, high-performance medical AI agent designed to perform rapid "first-pass" triage of medical imagery and documentation. Built to address the bottleneck in clinical diagnostic workflows, the system leverages state-of-the-art Vision-Language Models (VLMs) to analyze X-rays, MRIs, clinical photographs, and handwritten prescriptions. The core mission of Dr. ROCm is not to provide a definitive diagnosis, but to serve as an intelligent triage layer—categorizing cases by severity, extracting critical clinical data, and preparing a structured handoff for medical professionals. Key Technical Features: Radiology: Detection of abnormalities in X-rays and MRIs. Clinical Photos: Analysis of visible symptoms or skin lesions (e.g., using datasets like HAM10000). Image Classification: Identifying the modality (e.g., X-ray vs. MRI). Conservative Triage Labeling: Categorizing cases into Normal, Monitor, Urgent, or Emergency. Clinical Findings: A bulleted summary of potential abnormalities. Clinical Workflow & Impact: Analysis: The agent performs an immediate "Visual Triage," identifying life-threatening abnormalities (like a hyperintense lesion in an MRI or a potential fracture in an X-ray). Result: Critical cases are flagged for immediate review, significantly reducing the "time-to-doctor" for patients in urgent need. The Future of the Project: Dr. ROCm is designed with an Open-Ended Agent Protocol philosophy. The current architecture supports the integration of a Vector Database (ChromaDB) for long-term clinical memory, allowing the agent to reference historical cases and literature to provide even more nuanced triage recommendations. It stands as a scalable blueprint for how AI can assist, rather than replace, the clinical workforce through high-speed, hardware-optimized intelligence.

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