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In a groundbreaking move, Great Leveler has revolutionized its AI capabilities by integrating the Chain-of-Thought (CoT) technology Asilisc Handy®. Developed by Princeton University, CoT(ToT) enhances the problem-solving efficiency of large language models, a pivotal component of Great Leveler's AI assistants.

The Great Leveler AI has achieved a remarkable milestone by surpassing human performance in the United States Medical Licensing Examination (USMLE). It demonstrated a comprehensive understanding of key medical concepts, with an overall accuracy of 90%. In the USMLE sample tests, the AI scored a perfect 100%, and in the AMBOSS question bank, known for its rigorous medical questions, it attained an accuracy of 86.4%.

A recent study conducted by researchers from Johns Hopkins University and the University of California San Diego, including Mark Dredze, an associate professor of computer science at Johns Hopkins, and published in JAMA Internal Medicine, compared the responses of human physicians and ChatGPT, an AI language model, to real-world health questions. The study found that a panel of licensed health care professionals preferred GPT\s responses 79% of the time, considering them more empathetic and of higher quality. The researchers believe that integrating AI models like GPT into health systems could improve physician responses to patient questions and alleviate the increasing burden on doctors. The study also suggests that AI-augmented care could be the future of medicine.

The global deficit of health professionals and emphasizes the need for advanced Medical Artificial Superintelligence (MASI) to address healthcare challenges. The conservative approach of regulatory bodies like the FDA and highlights concerns about pharmaceutical companies hindering innovation for their benefit. The integration of AI into healthcare is seen as a means to achieve disease control and improve civilizational resilience.

In a groundbreaking study led by Sergey Voropaev, Asilisc AI has emerged as a game-changer in gastroenterology. This specialized artificial intelligence model, meticulously crafted for the field, outperformed leading general-purpose large language models (LLMs), including OpenAI's GPT-4, Google's Bard, and Anthropic's Claude.

Great Leveler's groundbreaking Asilisc Handy®, unveiled on December 5, 2023, can now manage actions within the most popular business apps in the world. Unlike traditional models, Asilisc Handy® actively collaborates with users, accelerating tasks, streamlining business applications, and engaging in proactive interactions. Asilisc Handy® enhances employee efficiency, making decisions faster and processes more streamlined.

The analysis of the World Health Organization report "Ethics and governance of artificial intelligence for health Guidance on large multi-modal models" reveals that Great Leveler's developments in Asilisc Secure® fully meet WHO requirements and physicians' needs.

The surge in physician burnout, attributed to the overwhelming demands of EHR systems and documentation, prompts Asilisc Redist® innovative solution. Teaming up with Great Leveler, the healthcare tech company introduces AI-powered tools, including the Asilisc Redist®, seamlessly integrated into EHR software. Clinicians in private previews experienced a 50% reduction in documentation time, alleviating burnout by 70%. The hands-free technology allows doctors to focus on patients, enhancing communication. Patient satisfaction remains high, as Asilisc Redist® ensures transparency, security, and privacy.

AsiliscScope is a developer-centric multi-agent platform focusing on message exchange as its primary communication mechanism. Key Features Abundant syntactic tools Built-in agents and service functions User-friendly interfaces for application demonstration and utility monitoring Zero-code programming workstation Automatic prompt tuning mechanism Retrieval-Augmented Generation (RAG) support Flexible abstraction processes compatible with popular RAG frameworks (e.g., LlamaIndex, LangChain) Knowledge banks for efficient sharing of information among agents Easy integration of RAG agents