On the Role of LLM to Forecast the Next Pandemic

Large Language Models (LLMs) have emerged as powerful tools in medical applications, demonstrating signifi-cant potential for analyzing vast clinical datasets, identifying patterns, and facilitating early detection of health anomalies. These capabilities are particularly relevant for detecting both individual disease onset and potential pandemic scenarios at a population level. The integration of LLMs into medical workflows could revolutionize how data-driven insights are harnessed for pandemic prediction and management, although implementing these technologies requires careful consideration of critical issues, particularly data privacy and security. This paper presents a comprehensive examination of current research at the intersection of LLMs and pandemic response, analyzing the literature from both bibliometric and medical perspectives. Through the selection of 849 publications across major databases, we provide an overview of the current state of research in this domain, identify emerging patterns from a clinical standpoint, and evaluate potential implications for future pandemic prediction. Our findings reveal significant trends in the application of LLMs to pandemic-related challenges, highlighting both opportunities and critical areas requiring further investigation, particularly in mental health impacts and early warning systems. The analysis reveals that while LLMs show promise in early detection and pattern recognition, challenges remain in data privacy, model interpretability, and the integration of diverse data sources. This research serves as a foundation for understanding how LLMs can be effectively deployed in pandemic prediction and management, while acknowledging the complexities and ethical considerations inherent in such applications.