Abstract

The application of AI (Artificial Intelligence) in emergency medicine helps significantly improve the quality of diagnostics under limitations of resources and time constraints in emergency cases. We have designed a comprehensive AI-based diagnostic and treatment plan decision-support system for emergency medicine by integrating the available LLMs (Large Language Models), like ChatGPT, Gemini, Claude, and others, and tuning them up with additional training on actual emergency cases. There is a special focus on early detection of life-threatening and time-sensitive diseases like sepsis, stroke, and heart attack, which are the major causes of death in emergency medicine. Additional training was conducted on a total of 600 cases (300 sepsis; 300 non-sepsis). The collective capability of the integrated LLMs is much stronger than each individual engine. Emergency cases can be predicted based on information from multiple sensors and streaming sources combining traditional IT (Information Technology) infrastructure with Internet of Things (IoT) schemes. Medical personnel compare and validate the AI models used in this work.