Limbix (by BigHealth)
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
The world's highest-performing open-source LLM suite for clinical reasoning and evidence-based medicine.
Meditron, developed by EPFL's Laboratory of Intelligent Systems, represents a pivotal shift in specialized vertical AI. Built upon the Llama 2 and Llama 3 architectures, Meditron is an open-source suite of large language models (7B and 70B parameters) specifically fine-tuned for the medical domain. Its 2026 market position is defined by its rigorous training on a curated 48.1 billion-token corpus, including peer-reviewed literature from PubMed, clinical guidelines from the Red Cross, and high-quality medical textbooks. Unlike general-purpose models, Meditron employs chain-of-thought (CoT) reasoning aligned with medical decision-making protocols. The architecture utilizes a low-rank adaptation (LoRA) strategy for efficient domain-specific fine-tuning, allowing it to outperform GPT-3.5 and Llama-base models on benchmarks like MedQA and MedMCQA. For the enterprise and healthcare provider, Meditron offers a sovereign alternative to proprietary medical APIs, ensuring data privacy through local deployment—a critical requirement for HIPAA and GDPR compliance in clinical environments. Its ability to synthesize evidence from diverse medical sources while maintaining a high degree of clinical accuracy makes it the preferred foundation for diagnostic support systems and medical research automation.
Meditron is trained on a filtered corpus of 48B tokens of high-quality clinical data, reducing the likelihood of hallucinations common in general LLMs.
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
Predictive clinical and operational intelligence to fight death and waste in healthcare.
Predictive medical data and clinical insights for streamlined enterprise underwriting.
The professional medical network for clinicians, providing HIPAA-compliant AI and telehealth solutions.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Incorporates specialized prompting techniques that force the model to trace diagnostic steps before providing a final answer.
Support for 4-bit and 8-bit quantization using GPTQ and AWQ, allowing the 7B model to run on consumer-grade hardware.
Pre-trained on medical literature spanning multiple languages, including non-English clinical guidelines.
Uses a specific instruction-following dataset derived from real-world clinical queries and physician responses.
Optimized for Retrieval-Augmented Generation, allowing seamless integration with real-time electronic health records (EHR).
Includes a built-in evaluation framework for testing against USMLE-style questions and clinical scenarios.
Assists physicians in narrowing down differential diagnoses for complex cases.
Registry Updated:2/7/2026
Summarizing thousands of new PubMed entries daily for specialist departments.
Translating dense medical jargon into accessible language for patient understanding.