|
| 1 | +--- |
| 2 | +layout: docs |
| 3 | +header: true |
| 4 | +seotitle: Medical LLMs| John Snow Labs |
| 5 | +title: Medical LLMs |
| 6 | +permalink: /docs/en/LLMs/medical_llm |
| 7 | +key: docs-medical-llm |
| 8 | +modify_date: "2025-03-31" |
| 9 | +show_nav: true |
| 10 | +sidebar: |
| 11 | + nav: medical-llm |
| 12 | +--- |
| 13 | + |
| 14 | +There is overwhelming evidence from both academic research and industry benchmarks that domain-specific, task-optimized large language models consistently outperform general-purpose LLMs in healthcare. At John Snow Labs, we’ve developed a suite of Medical LLMs purpose-built for clinical, biomedical, and life sciences applications. |
| 15 | + |
| 16 | +Our models are designed to deliver best-in-class performance across a wide range of medical tasks—from clinical reasoning and diagnostics to medical research comprehension and genetic analysis. |
| 17 | + |
| 18 | +## Medical LLMs Offering |
| 19 | + |
| 20 | +| **Model Name** | **Parameters** | **Recommended GPU Memory** | **Max Sequence Length** | **Model Size** | **Max KV-Cache** |**Tensor Parallel Sizes**| |
| 21 | +| Medical-LLM-7B | 7B | ~25GB | 32K | 14GB |10.50 GB | 1,2,4 | |
| 22 | +| Medical-LLM-10B | 10B | ~35GB | 32K | 19GB |15.00 GB| 1,2,4 | |
| 23 | +| Medical-LLM-14B | 14B | ~40FB | 16K | 28GB | 12.50GB | 1,2 | |
| 24 | +| Medical-LLM-24B | 24B | ~70GB | 32K | 44GB | 25GB | 1,2,4,8 | |
| 25 | +| Medical-LLM-Small | 14B | ~58GB | 32K | 28GB | 30GB | 1,2,4,8 | |
| 26 | +| Medical-LLM-Medium | 70B | 452GB | 128K | 132GB | 320GB | 4, 8 | |
| 27 | + |
| 28 | + |
| 29 | +*Note: All memory calculations are based on half-precision (fp16/bf16) weights. Recommended GPU Memory considers the model size and the maximum key-value cache at the model's maximum sequence length. These calculations follow the guidelines from [DJL's LMI Deployment Guide.](https://docs.djl.ai/master/docs/serving/serving/docs/lmi/deployment_guide/instance-type-selection.html)* |
| 30 | + |
| 31 | +## Introduction |
| 32 | +John Snow Labs’ latest 2025 release of its Medical Large Language Models advance Healthcare AI by setting new state-of-the-art accuracy on medical LLM benchmarks. This advances what’s achievable in a variety of real-world use cases including clinical assessment, medical question answering, biomedical research synthesis, and diagnostic decision support. |
| 33 | + |
| 34 | +Leading the pack is their largest 70B model, which can read and understand up to 32,000 words at once – that’s roughly 64 pages of medical text. The model is specially trained to work with medical information, from patient records to research papers, making it highly accurate for healthcare tasks. What makes this release special is how well the model performs while still being practical enough for everyday use in hospitals and clinics – thanks to a suite of models in different sizes, that balance accuracy with speed, cost, and privacy. |
| 35 | + |
| 36 | +## OpenMed Benchmark Performance |
| 37 | +The comprehensive evaluation of John Snow Labs’ Medical LLM suite encompasses multiple standardized benchmarks, providing a thorough assessment of their capabilities across various medical domains. These evaluations demonstrate not only the models’ proficiency in medical knowledge but also their practical applicability in real-world healthcare scenarios. |
| 38 | + |
| 39 | +The OpenMed evaluation framework represents one of the most rigorous testing environments for medical AI models, covering a broad spectrum of medical knowledge and clinical reasoning capabilities. Our models have undergone extensive testing across multiple categories, achieving remarkable results that validate their exceptional performance: |
| 40 | + |
| 41 | +## Model Performance Matrix |
| 42 | +**Large (70B+) Models Comparison** |
| 43 | + |
| 44 | + |
| 45 | + |
| 46 | +**Smaller Models Comparison** |
| 47 | + |
| 48 | + |
| 49 | + |
| 50 | +All scores are presented as percentages (%) |
| 51 | + |
| 52 | + |
| 53 | + |
| 54 | +## Open Medical Leaderboard Performance Analysis |
| 55 | + |
| 56 | +John Snow Labs’ Medical LLMs have been rigorously evaluated against leading general-purpose and medical-specific models, including GPT-4 and Med-PaLM-2. Here's a detailed breakdown of their performance across key medical domains: |
| 57 | + |
| 58 | +1. **Clinical Knowledge** |
| 59 | + |
| 60 | + - Outperforms GPT-4 in clinical knowledge assessment (89.43% vs 86.04%) |
| 61 | + |
| 62 | + - Shows stronger diagnostic and treatment planning capabilities |
| 63 | + |
| 64 | +2. **Medical Genetics** |
| 65 | + |
| 66 | + - Exceeds both GPT-4 and Med-PaLM-2 in genetic analysis (95% vs 91% and 90%) |
| 67 | + |
| 68 | + - Demonstrates advanced understanding of genetic disorders and inheritance patterns |
| 69 | + |
| 70 | +3. **Medical Knowledge: Anatomy** |
| 71 | + |
| 72 | + - Superior anatomical knowledge compared to both alternatives (85.19% vs 80% and 77.8%) |
| 73 | + |
| 74 | + - Shows stronger grasp of structural and functional relationships |
| 75 | + |
| 76 | +4. **Clinical Reasoning: Professional Practice** |
| 77 | + |
| 78 | + - Surpasses GPT-4 in professional medical scenarios (94.85% vs 93.01%) |
| 79 | + |
| 80 | + - Better understanding of medical protocols and clinical guidelines |
| 81 | + |
| 82 | +5. **Cross-Domain Capability: Life Sciences** |
| 83 | + |
| 84 | + - Slightly lower than GPT-4 but comparable to Med-PaLM-2 (93.75% vs 95.14% and 94.4%) |
| 85 | + |
| 86 | + - Strong foundation in biological sciences and medical principles |
| 87 | + |
| 88 | +6. **Medical Knowledge: Core Concepts** |
| 89 | + |
| 90 | + - Significantly outperforms both models (83.24% vs 76.88% and 80.9%) |
| 91 | + |
| 92 | + - Better understanding of fundamental medical concepts |
| 93 | + |
| 94 | +7. **Clinical Case Analysis** |
| 95 | + |
| 96 | + - Slightly better performance in clinical case scenarios (79.81% vs 78.87% and 79.7%) |
| 97 | + |
| 98 | + - More accurate in diagnostic decision-making |
| 99 | + |
| 100 | +8. **Medical Research Comprehension** |
| 101 | + |
| 102 | + - Notable improvement over GPT-4 in research analysis (79.4% vs 75.2%) |
| 103 | + |
| 104 | + - Better at interpreting medical literature and research findings |
| 105 | + |
| 106 | +9. **Clinical Assessment** |
| 107 | + |
| 108 | + - Substantially higher performance in clinical assessments (75.45% vs 69.52% and 71.3%) |
| 109 | + |
| 110 | + - Superior ability in evaluating clinical scenarios and treatment options |
| 111 | + |
| 112 | + |
| 113 | +## Small Yet Powerful: Efficiency Meets Performance |
| 114 | + |
| 115 | +One of the standout features of John Snow Labs' Medical LLMs is their efficiency at scale. These models deliver exceptional performance without requiring massive infrastructure: |
| 116 | + |
| 117 | +- Designed to run efficiently on a range of GPU configurations |
| 118 | + |
| 119 | +- Available in multiple sizes (7B, 10B, 14B, 24B, 70B) to suit different deployment needs |
| 120 | + |
| 121 | +- Optimized for both on-premise and private cloud deployments |
| 122 | + |
| 123 | +💡 You can achieve cutting-edge performance in clinical NLP without the costs and risks of using massive general-purpose models. |
| 124 | + |
| 125 | + |
| 126 | + |
| 127 | +💡The figures demonstrate the comparative performance metrics of our models across key medical benchmarks and clinical reasoning tasks. |
| 128 | + |
| 129 | + |
| 130 | + |
| 131 | + |
| 132 | + |
| 133 | + |
| 134 | +**Medical-LLM – 14B** |
| 135 | + - Achieves 81.42% average score vs GPT-4’s 82.85% and Med-PaLM-2’s 84.08% |
| 136 | + - Clinical knowledge score of 92.36% vs Med-PaLM-2’s 88.3% |
| 137 | + - Medical reasoning at 90% matches Med-PaLM-2’s performance |
| 138 | + - Higher accuracy than Meditron-70B while using 5x less parameters |
| 139 | + - Suitable for deployment scenarios with compute constraints |
| 140 | + |
| 141 | +**Medical-LLM – 10B** |
| 142 | + |
| 143 | + - Average score of 75.19% across medical benchmarks |
| 144 | + - Clinical analysis score of 88.19% vs Med-PaLM-1’s 83.8% |
| 145 | + - Medical Genetics score of 82% vs Med-PaLM-1’s 75% |
| 146 | + - Comparable performance to models requiring 7x more parameters |
| 147 | + - Balanced option for resource-conscious implementations |
| 148 | + |
| 149 | +**Medical-LLM – 7B** |
| 150 | + |
| 151 | +- Clinical reasoning score of 86.81% vs Med-PaLM-1’s 83.8% |
| 152 | +- Average score of 71.70% on OpenMed benchmark suite |
| 153 | +- PubMedQA score of 75.6%, higher than other 7B models |
| 154 | +- Matches GPT-4’s accuracy on medical QA with 100x fewer parameters |
| 155 | +- Efficient choice for high-throughput clinical applications |
| 156 | + |
| 157 | +## Performance-to-Size Comparison |
| 158 | + |
| 159 | + |
| 160 | + |
| 161 | +These models are available for on-premise deployment as well as through leading cloud marketplaces, making deployment and integration straightforward for healthcare organizations. The marketplace availability ensures scalable access to these state-of-the-art medical AI capabilities, with enterprise-grade security and compliance features built-in. Organizations can leverage these models through flexible consumption-based pricing models, enabling both small-scale implementations and large enterprise deployments. |
| 162 | + |
| 163 | + |
| 164 | +## Partner With Us |
| 165 | + |
| 166 | +We’re committed to helping you stay at the cutting edge of medical AI. Whether you’re building decision support tools, clinical chatbots, or research platforms — our team is here to help. |
| 167 | + |
| 168 | + [Book a call with our experts to:](https://www.johnsnowlabs.com/schedule-a-demo/) |
| 169 | +- Discuss your specific use case |
| 170 | +- Get a live demo of the Medical LLMs |
| 171 | +- Explore tailored deployment options. |
0 commit comments