Search for a command to run...
Developments in artificial intelligence have made bio-cognitive systems integrating biological inspirations, cognitive computing, and data-driven intelligence to improve clinical decision-making possible. The paper suggests a new Bio-Cognitive AI design of predictive healthcare decision support that integrates multimodal patient data, physiological cues, electronic health records (EHR) and longitudinal biomarkers through a hybrid system, which combines deep neural network, cognitive reasoning, and adaptive learning engines. The system relies on early alert warnings of chronic diseases, contextual inference and explainable prediction to provide predictive layers, hierarchical feature abstraction, and patient risk trajectory assessment and optimal intervention pathway recommendations. The framework has interpretable AI models, quantification of uncertainty, federated learning with privacy assurance, and reinforcement learning to refine continuous performance to achieve trust, robustness, and clinical adoption. Experimental analysis of actual healthcare data show that prediction accuracy, sensitivity and time-to-detection significantly improve when using experimental evaluation with real world data. The suggested Bio-Cognitive AI is a clinically reliable and secure next-generation technology to support the management of diseases proactively and make more informed medical decisions because this solution is scalable.