Medical-Visual-LLM-30B

**Medical-Visual-LLM-30B**

This 30B parameter medical vision language foundation model seamlessly integrates advanced medical reasoning with powerful visual understanding.

Designed specifically for the healthcare domain, it can interpret and analyze both textual and visual medical data, including clinical notes, lab reports, X rays, MRIs, CT scans, pathology slides, and anatomical diagrams.

By combining domain-specific medical expertise with multimodal comprehension, the model enables deeper insights across diagnostic, research, and clinical workflows. Its dual modality architecture allows it to jointly process patient text records and visual imaging data, offering physicians a unified perspective for accurate, context aware decision making. It can summarize complex clinical documents, generate detailed yet concise medical reports, and answer domain specific questions with high factual precision while maintaining critical nuance and detail.

The model supports extended medical reports, multi image cases, and long contextual reasoning in a single prompt.

Optimized for RAG and integration with electronic health records and imaging systems, it delivers informed, evidence grounded responses that bridge the gap between visual diagnostics and textual analysis, advancing the future of intelligent medical assistance.

**Benchmarks and other characteristics:**
- Achieves 83.5% average across OpenMed benchmarks
- Scores 85.66% on clinical knowledge assessment
- Reaches 95% on medical genetics understanding
- Performs at 93.75% for college biology concepts
- Processes professional medicine with 89.34% accuracy
- Handles medical MCQAs with 68.8% precision
- Maintains 77.61% accuracy on MedQA 4-options test

**Performance metrics:**

**Medical-Visual-LLM-30B** 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 **GPU_MEDIUM_8** configuration using four NVIDIA A10 GPUs with a combined memory capacity of 192 GB, and with the model configured for long-context inference (maximum context length of 262,144 tokens).

Under identical infrastructure and model settings:

- **Question Answering workload** achieved an average of 440 tokens per second
- **Summarization workload** achieved an average of 95 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**
- [Multimodal Capability of the Medical Vision-Language Models ](https://www.johnsnowlabs.com/introducing-medical-vlm-24b-our-first-medical-vision-language-model/)
- [Measuring the Benefits of Healthcare Specific Large Language Models](https://www.nlpsummit.org/measuring-the-benefits-of-healthcare-specific-large-language-models/)
- [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/)

**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.