Medical LLM - 24B

Matching the performance of industry leaders with quicker inference times and lower deployment costs, the model is ideal for healthcare institutions needing medical analysis without substantial computational resources.

Trained on a wide array of medical texts, the model is highly adept at summarizing complex clinical information, answering medical questions, and summarizing detailed clinical notes and various medical reports.

Its summarization capability enhances efficiency while retaining essential details. The model's ability to answer both open and closed medical queries with precise, context-specific responses further aids in clinical decision-making. It provides physicians with rapid insights into a patient's medical history, enabling timely and well-informed decisions. Optimized for Retrieval-Augmented Generation (RAG), it works effectively with healthcare databases, EHR systems, and scientific literature repositories to improve the quality of responses.

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**Accuracy**:

- Achieves 82.83% OpenMed average, matching GPT-4 (82.85%) with fraction of parameters.
- Outperforms Med-PaLM-2 in medical genetics (92% vs 90%) and college medicine (79.19% vs 80.9%)
- Matches GPT-4's clinical knowledge accuracy (86.04%)
- Surpasses Med-PaLM-1 across all benchmarks by +8.13% average
- Processes medical MCQAs with 70.27% accuracy, comparable to Med-PaLM-2 (71.3%)
- Maintains competitive performance with leading models while requiring significantly less compute