AI-Driven Evolutionary Computation in Medical Robotics: A Predictive Approach to Healthcare Monitoring and Crisis Prevention

The integration of Artificial Intelligence (AI) and Evolutionary Computation (EC) in medical robotics hassignificantly transformed modern healthcare, offering real-time patient monitoring, predictive analysis,and efficient medical decision-making. This research explores how AI-driven micro-training and roboticscan automate real-time health examinations by tracking key vitals such as heartbeat, pulse rate, andblood cell count. Additionally, environmental data from advanced medical infrastructures worldwide willbe analyzed to predict potential health crises, such as the impact of air pollution on asthma patients orthe risks posed by seasonal outbreaks. By developing an AI-driven predictive model, this study aims toprovide early warnings for high-risk patients, helping to prevent medical emergencies. The proposedframework employs multi-agent AI models to optimize real-time medical data processing withoutoverburdening computational servers. This study also evaluates the ethical considerations, privacyconcerns, and implementation challenges in large-scale healthcare systems.