Sepsis, a serious organ dysfunction syndrome caused by infection, is one of the leading causes of human mortality and disability worldwide, and accounts for an in-hospital death rate of 10-20%. The situation is worse in children, especially newborn, where death due to neonatal sepsis was almost 16% of total neonatal mortality in 2013.

Biomarkers, which are indicators of biological states or conditions, have been widely used to improve the diagnosis, therapy and prognosis of human sepsis. Thus, many researchers including our group have focused on the identification of sepsis biomarkers . Until 2018-11-28, 8936 related papers were reported on the Web of science database.

Retrieval-Augmented Generation (RAG) is an advanced AI framework that combines the strengths of information retrieval and generative models to enhance the accuracy and relevance of generated responses. Unlike traditional generative models that rely solely on pre-trained knowledge, RAG dynamically retrieves relevant documents or knowledge from external sources before generating an output. This approach improves factual accuracy, reduces hallucinations, and ensures that responses are grounded in reliable information. In the healthcare domain, RAG holds great promise for various applications: 1. Clinical Decision Support - RAG can assist clinicians by retrieving the latest medical literature, guidelines, and patient-specific data to generate evidence-based recommendations for diagnosis and treatment. 2. Biomedical Research Assistance - Researchers can use RAG to efficiently extract and summarize key findings from vast biomedical databases, accelerating discoveries in disease mechanisms, biomarkers, and drug development. 3. Personalized Medicine - By integrating patient data with real-time knowledge retrieval, RAG can help tailor treatment strategies based on individual patient characteristics, supporting precision medicine. 4. Medical Documentation and Coding - RAG can streamline medical documentation by retrieving relevant information from patient records and generating structured reports, reducing administrative burdens. 5. Patient Education and Chatbots - AI-driven healthcare assistants powered by RAG can provide patients with accurate, context-aware answers to their medical queries, improving health literacy and engagement.

As RAG technology evolves, its integration with domain-specific knowledge bases, ontologies, and real-world clinical data will further enhance its reliability, paving the way for more explainable and trustworthy AI-driven healthcare solutions.