Medical Visual LLM - 8B

**Medical Visual LLM - 8B**

This medical vision-language model is designed to bring medical-grade multimodal intelligence into a smaller, more efficient package.

The model not only understands medical text but also interprets visual information across scans, charts, diagrams, and structured documents. It can analyze **X-rays, MRIs, pathology slides, medical diagrams,** and even patient records containing forms, tables, or structured data.

Compact yet powerful, the model is optimized for efficiency while retaining the ability to summarize clinical information, answer contextual medical questions, and facilitate decision-making. It is particularly well-suited for scenarios that demand a balance of speed, accuracy, and multimodal understanding across both text and medical visuals.

With 32K context window, it processes longer documents.

Optimized for RAG applications, it integrates with healthcare databases and imaging systems to deliver informed responses across both visual and textual medical domains.

**Benchmarks and other characteristics:**
- Achieves 90.8% average across OpenMed benchmarks
- Scores 87% on clinical knowledge assessment
- Reaches 98% on medical genetics understanding
- Performs at 97.8% for college biology concepts
- Processes professional medicine with 93.5% accuracy
- Handles medical MCQs with 92% precision
- Maintains 89.8% accuracy on Anatomy concepts

**Performance metrics:**

**Medical Visual LLM - 8B** 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 128K tokens).

Under identical infrastructure and model settings:
- **Question Answering workload** achieved an average of 750 tokens per second
- **Summarization workload** achieved an average of 250 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.