Trained on diverse medical texts, this model excels in summarizing, answering complex clinical questions, and transforming clinical notes, patient encounters, and medical reports into concise summaries. Its question-answering capability ensures context-specific responses, enhancing decision-making. For physicians, this tool offers a quick grasp of a patient’s history, aiding timely decisions. Optimized for Retrieval-Augmented Generation (RAG), the model integrates with healthcare databases, EHRs, and PubMed to boost response quality. For enhanced patient care, we offer clinical de-identification for secure data processing, medical speech-to-text for accurate transcriptions, and a medical chatbot to facilitate patient interaction. Benchmarking Results: Achieves 86.31% average on OpenMed benchmarks, surpassing GPT-4 (82.85%) and Med-PaLM-2 (84.08%) Performance in medical genetics: 95%; performance in professional medicine: 94.85% Clinical knowledge comprehension 89.81% and college biology mastery 93.75% Achieves 58.9% average on standard LLM benchmarks Balance of specialized medical knowledge and broad language understanding, demonstrated by 70.93% on GPT4All benchmark Achieves 75.54% performance in medical MCQAs and 79.4% on PubMedQA Recommended Instance type for this model: Standard_NC96ads_A100_v4