Yesil o1 Pro: Evidence-Based AI Model for Health and Benchmarking in Clinical Decision Support (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Integrating evidence-based approaches in healthcare and artificial intelligence (AI) is crucial for enhancing clinical decision-making and patient safety. Yesil o1 Pro is a specialized large language model (LLM) designed to transform medical knowledge synthesis by leveraging a comprehensive, curated database of scientific literature, clinical guidelines, and medical textbooks. </sec> <sec> <title>OBJECTIVE</title> The system's innovative "AI Hospital" framework employs domain-specific expert agents coordinated by a central Master Agent, enabling tailored and precise medical responses across multiple disciplines. </sec> <sec> <title>METHODS</title> The model's advanced methodology incorporates sophisticated techniques including GraphRAG-based retrieval, extensive fine-tuning with 1.5 million question-answer pairs, and Chain of Thought (CoT) reasoning. Its robust training dataset comprises 100.5M words from high-impact journals, 96.8M words from core medical texts, and 74.4M words from international standards, ensuring a comprehensive and authoritative knowledge base. </sec> <sec> <title>RESULTS</title> Benchmark evaluations demonstrate Yesil o1 Pro's exceptional performance, achieving an overall accuracy of 89.1% and surpassing leading models like GPT-4o (83.9%) and Claude 3.5 Sonnet (83.0%). Domain-specific accuracies are particularly impressive, with 96.1% in Mental Health, 94.6% in Epidemiology, and 94.6% in Dentistry, highlighting the model's proficiency in handling complex, reasoning-intensive medical queries. </sec> <sec> <title>CONCLUSIONS</title> The model shows promising applications in clinical decision support, interdisciplinary collaboration, professional education, and medical research. While challenges remain in real-world integration and maintaining alignment with evolving medical knowledge, Yesil o1 Pro represents a significant advancement in AI-driven healthcare support. Future research will focus on validating the model's utility in clinical environments and developing strategies for seamless healthcare system integration. </sec>

Ähnliche Arbeiten