AI-Enabled Precision Medicine: Optimizing Treatment Strategies Through Genomic Data Analysis

This research investigates how Artificial Intelligence (AI) can be used in precision medicine to improve treatment strategies by analysing genomic data. We explore sophisticated machine learning methods for examining complicated genomic datasets, such as deep learning models and ensemble techniques. The study focuses on overcoming obstacles in analysing complex genomic data and introduces innovative methods for combining multi-omics data. We create predictive models using AI technology to forecast patient prognosis with better accuracy in predicting disease progression and treatment results. The research also looks into AI's use in finding new uses for drugs, demonstrating how machine learning can speed up the process of finding potential treatments. We introduce a model for personalised treatment planning using AI, which integrates genomic biomarkers and clinical factors to enhance drug combination and dosage selection. The assessment of incorporating AI in clinical decision support systems is conducted, showcasing enhancements in diagnostic accuracy and treatment effectiveness through multiple medical fields. Ethical concerns, such as algorithmic bias and data privacy, are thoroughly examined, focusing on discussing regulatory guidelines for AI in the healthcare sector. Our results suggest precision medicine with AI technology could significantly improve treatment customisation, enhance patient results, and transform healthcare delivery. Nevertheless, it is essential to consider ethical, privacy, and regulatory obstacles when implementing responsible practices.