Adaptive Intelligence in Personalized Medicine: A Hybrid Computational Framework

Abstract

The rapid progress of technology in the medical field has led to the era of prescriptive medicine where data collection represents foundation for individual patient diagnosis, treatment, and prognosis. In this paper, an Adaptive Intelligence Hybrid Computational Framework is presented, which is going to be a combination of machine learning, bioinformatics, and artificial intelligence (AI) techniques, and its aim is to help making decisions that are adaptive in personalized healthcare. The framework relies on multi-modal data—genomics, proteomics, medical imaging, and electronic health records being just a part of the whole—that are employed to build patient profiles which are not only dynamic but also capable of changing their properties as they gain knowledge through time. The proposed system, utilizing various computational models like deep neural networks, fuzzy inference systems, and evolutionary algorithms, is now capable of real-time optimization of therapeutic strategies and predictive diagnostics.

The experimental results have indicated that the new method has a significant advantage over the traditional AI methods in terms of accuracy, interpretability, and adaptability. Therefore, the framework is regarded as a major player in various clinical application such as early disease detection, drug response prediction, and patient-specific treatment optimization; thus, it is transforming the precision and patient-centric healthcare systems.