• broken image

    The best healthcare-specific large language model (LLM)

    LLM at the speed and grace of a gazelle or "gazal" in Arabic language

  • Why gazal

    The healthcare industry, as we know it, faces monumental challenges. From inefficiencies in data management to the overwhelming volume of medical knowledge and the critical need for more intelligent care

    broken image

    At the edge

    Gazal was built for the world of Large Language Models (LLMs) with the unique ability to understand, interpret, and generate human-like text based on vast amounts of data

    broken image

    Secure for health

    At gazal, we are committed to navigating the ethical landscape of healthcare with utmost care. Ensuring privacy, security, and unbiased, equitable access to AI solutions to the entire ecosystem

    broken image

    Ultra efficient

    Leveraging open-source state of the art (SOTA) LLMs that are fine-tuned for specific healthcare tasks enables you to achieve optimal outcomes using minimal computational resources

  • A sneak peak into the future

    Join the revolution, explore the potential use cases, and be part of the transformation. Sign up for early access and an opportunity to shape the future of healthcare innovation.

  • Gazal with Mixtral

    The fine-tuned version of Mixtral-8x7B on open-source medical and healthcare dataset to perform variety of healthcare-specific language tasks. The base model, Mixtral, is a high-quality sparse mixture of experts model (SMoE) with open weights developed by Mistral AI. This model outperforms Llama2 70B on most benchmarks with 6x faster inference.

     

     

  • Health as it should be

    Healthcare professionals and LLMs can work together in a synergistic manner, enhancing healthcare delivery, patient care, and medical research

    Medical Coding

    By automating code suggestions, continuously adapting to new standards, and providing error detection capabilities, LLMs can streamline the coding process, reduce manual effort, and ensure compliance with current regulations with their advanced natural language processing abilities

    Administrative Tasks Automation

    Many administrative tasks, such as scheduling appointments, billing, and maintaining patient records, can be streamlined or automated with LLMs. This reduces the workload on healthcare staff, allowing them to dedicate more time to direct patient care

    Personalized Health Education

    LLMs can generate personalized educational materials for patients, explaining their conditions, treatment plans, and preventive measures in an understandable way. This helps in empowering patients with knowledge about their health

  • Watch gazal in action

    Gazal is continuously learning, growing, and evolving in sophistication and healthcare use cases. We are engaging in ongoing fine-tuning and applying Retrieval-Augmented Generation (RAG) to state-of-the-art base models to develop the best healthcare-specific LLM, gazal. Meet some use-cases here.

    Medical text summarization

    Gazal streamlines your workflow by condensing lengthy medical information from various sources into concise, easily digestible summaries, ensuring key information is accessible at a glance. It highlights critical points and findings with precision, working across patient records, research articles, and medical guidelines in any format without compromising accuracy.

    Medical coding and billing

    Gazal leverages LLMs to revolutionize medical coding, streamlining the extraction of diagnoses, procedures, services, and medication codes with unmatched precision and efficiency. It interprets complex clinical documents, extracting essential details for accurate coding, reducing errors, and improving billing accuracy. It offers real-time suggestions, keeping pace with standards, thus boosting efficiency and compliance.

    Health questions answering

    Gazal supports clinical knowledge by providing answers to questions with speed and accuracy ONLY from reliable sources and evidence based guidelines. Here, it navigates through PubMed to offer clear and hallucination-free response. It picks questions across diverse scenarios, ensuring you have the right answers when you need them most, thus improving access to reliable knowledge.

  • Bringing reliability to LLM

    High-quality data leads to high-quality results. By expanding our healthcare data lakes, we're advancing the development of responsible and reliable healthcare AI, ensuring our models are grounded on robust and credible information

    70 Billion

    The size of parameters of the LLM we successfully fine-tuned so far

    2 Billion

    Healthcare specific tokens for training and fine-tuning in our datasets

    575 Million

    The size of the high-quality clean healthcare data in structured and tabular format

    +100

    Sources from credible, evidence-based scientific research and open data sources

  • Join the future of healthcare

    Get a sneak peek at what we're working on by contacting us here

  • FAQs

  • Check out our latest blogs!

    Stay updated with the latest in AI healthcare innovation, company news, and expert insights.

Cookie Use
We use cookies to ensure a smooth browsing experience. By continuing we assume you accept the use of cookies.
Learn More