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: In a randomized blind evaluation by medical experts, this model outperformed GPT-4o on: Text Summarization: Preferred 88% more often on factuality, 92% on relevance, 68% on conciseness. Clinical Notes Question Answering: Preferred 46% more on factuality, 50% on relevance, 44% on conciseness. Biomedical Research Question Answering: Preferred 175% more on factuality, 200% on relevance, 256% on conciseness. General Knowledge Question Answering: Comparable performance to GPT-4o. Recommended Instance for this model is Standard_NC40ads_H100_v5 or Standard_NC24ads_A100_v4