Medical LLM - Small

**Medical LLM - Small**

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. It introduces a dedicated reasoning mode that can follow multi-step clinical logic and justify its answers.

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 medical history, aiding timely and informed decisions. Instead of sifting through extensive documentation, doctors can rely on these summaries to understand a patient 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.

**Benchmarks and other characteristics::**
- 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

**Performance metrics:**

**Medical-LLM-Small** model was evaluated using a chat-based completion workflow across two representative subtasks: **Question Answering** and **Summarization**. Both benchmarks were executed against the same dataset of 100 documents, processed in five invocations with 20 documents per request, ensuring consistent workload characteristics across tests.

The experiments were conducted under a **MULTIGPU_MEDIUM** configuration using four NVIDIA A10 GPUs with a combined memory capacity of 96 GB, and with the model configured for long-context inference (maximum context length of 40,960 tokens).

Under identical infrastructure and model settings:
- **Question Answering workload** achieved an average of 520 tokens per second
- **Summarization workload** achieved an average of 150 tokens per second

See this table for approximate [memory calculations required](https://nlp.johnsnowlabs.com/docs/en/LLMs/medical_llm#medical-llms-offering) to use this model.

**Additional Model Information**

- [John Snow Labs New Suite of Medical Language Models Advance Industry Benchmarks](https://www.johnsnowlabs.com/john-snow-labs-new-suite-of-medical-language-models-advance-industry-benchmarks/)
- [Measuring the Benefits of Healthcare Specific Large Language Models](https://www.nlpsummit.org/measuring-the-benefits-of-healthcare-specific-large-language-models/)

**How to run this model**:

1. Acquire a John Snow Labs Pay As You Go (PAYG) license from [Sales]([e-mail])
2. Import this listing.
3. See the attached notebook to deploy and use the model.

**Vendor support**
For any assistance, please reach out to [e-mail]

This model comes with optimized CPU and GPU builds. You can select which one to deploy via the notebook.