AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNING

Abstract

An effective emergency and disaster response plan is critical to healthcare management, ensuring timely intervention and mitigating adverse consequences. Integrating Artificial Intelligence (AI) and Machine Learning (ML) into planning, resource allocation, disaster simulation, real-time decision-making, communication, coordination, and ethical considerations presents significant opportunities for enhancing healthcare emergency preparedness. This review examines five key research questions regarding the current state of AI/ML applications in healthcare emergencies and disaster response. A systematic literature review was conducted to identify relevant studies and insights for improving emergency healthcare responses through AI/ML advancements. Findings suggest that AI/ML optimizes resource allocation by leveraging predictive analytics and logistics management, ensuring efficient medical supplies and personnel distribution during crises. AI-driven simulation models enhance preparedness by analyzing various disaster scenarios, aiding in strategic decision-making. Additionally, AI improves real-time information sharing and operational coordination among emergency response teams, facilitating more efficient crisis management. However, ethical concerns surrounding AI implementation, particularly regarding privacy, accountability, and bias, must be carefully addressed. This study underscores the transformative potential of AI/ML in emergency and disaster response, highlighting its benefits and challenges. While AI integration promises improved operational efficiency and enhanced healthcare responses, careful consideration of ethical, regulatory, and practical implications is necessary to ensure responsible deployment.