Self-Evolving Autonomous Healthcare System Using Continual Learning and Adaptive Intelligence
DOI:
https://doi.org/10.5281/ijurd.v2i3.25Keywords:
Hospital Readmission, Ensemble Learning, Random Forest, XGBoost, Healthcare Analytics, Predictive ModelingAbstract
Modern healthcare systems require intelligent solutions that can continuously learn and adapt to evolving medical knowledge and patient conditions. This paper presents a Self-Evolving Autonomous Healthcare System using Continual Learning and Adaptive Intelligence to enable long-term, dynamic learning without performance degradation. The proposed framework integrates continual learning techniques with deep neural networks to update models incrementally as new patient data becomes available, avoiding catastrophic forgetting. The system processes multi-modal healthcare data, including clinical records, medical images, and real-time sensor data, to support disease prediction and personalized treatment recommendations. Adaptive intelligence mechanisms are incorporated to adjust model behavior based on changing data distributions and emerging health trends. The framework also includes explainability and safety modules to ensure transparency and reliability in clinical applications. Experimental results demonstrate improved model stability, adaptability, and predictive accuracy compared to static learning approaches. Integration with prior research in healthcare analytics further enhances robustness and scalability. The study highlights the potential of self-evolving AI systems in transforming healthcare into a continuously learning, proactive, and intelligent ecosystem capable of addressing future medical challenges.
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Copyright (c) 2026 Mona Shukla, Ramesh Shah
This work is licensed under a Creative Commons Attribution 4.0 International License.
