Built an end-to-end Clinical Decision Support System that transforms raw, unstructured medical notes into actionable intelligence. By combining domain-specific BERT architectures with advanced Generative AI, this pipeline acts as an automated clinical assistant capable of diagnosing, routing, and recommending treatments based on complex patient histories. 1. Processed 4,000+ MTSamples transcriptions across 13 medical specialties into structured intelligence. 2. Fine-tuned BioClinicalBERT (on i2b2/n2c2 corpora) for Named Entity Recognition (NER) to extract medical problems, tests, and administered treatments with an F1 score > 0.87. 3. Deployed a PubMedBERT-based multi-class classifier achieving 93% accuracy in routing clinical notes to the correct specialty. 4. Orchestrated Google's Gemma3-4B via local Ollama REST APIs, enforcing strict JSON schemas to perform advanced clinical reasoning and infer comprehensive, step-by-step treatment plans. 5. Built an interactive Streamlit UI, implementing proactive GPU memory management (VRAM flushing) to run this massive multi-stage transformer pipeline smoothly on local hardware.