Trained on diverse medical texts, this model excels in summarizing, answering complex clinical questions, and transforming detailed clinical notes, patient encounters, and various medical reports into concise, digestible summaries. The summarization feature boosts efficiency while preserving critical details, supporting optimal patient care. Its question-answering capability ensures accurate, context-specific responses to both open and closed medical queries, further enhancing decision-making. For physicians, this tool offers a quick grasp of a patient’s medical history, aiding timely and informed decisions. Instead of sifting through extensive documentation, doctors can rely on these summaries to understand a patient’s journey, condition, and treatment protocols swiftly. Optimized for Retrieval-Augmented Generation (RAG), the model can be used in combination with healthcare databases, EHR, and scientific literature repositories (like PubMed) to enhance response quality. Benchmarking Results: Achieves 81.42% average, competing with GPT-4 (82.85%) Outstanding clinical comprehension (93.40%), exceeding Med-PaLM-2's 88.3% Superior medical reasoning (90%) comparable to top-tier models Outperforms Meditron-70B despite being 5x smaller State-of-the-art performance in medical tasks while maintaining deployment efficiency Recommended Instance for this model is Standard_NC40ads_H100_v5 or Standard_NC24ads_A100_v4