DefinePK

Abstract

Artificial intelligence and machine learning integration in biomedical research has tremendously benefitted precision medicine, disease diagnosis, and drug discovery. On the basis of these four advanced algorithms, this study investigates how AI-driven methodologies can be used for analysis in medical imaging, processing of genomic data and the prediction of drug response. Results from the experimental results show that traditional methods fail with a diagnostic accuracy of 82.7 % while Deep Learning-based medical imaging models attain a diagnostic accuracy of 97.3% outperforming the traditional methods by 15%. AI based genomic data mining had helped improve the mutation detection rate by 18%, which improved precision medicine approaches. Predictive models in cancer immunotherapy also increased treatment success rates by 22% in AI’s study. In addition, applying reinforcement learning in drug discovery led to compound screening efficiency of 40% improvement and reduced total drug development time. This underscores AI’s ability to increase diagnostic precision, improve treatment strategies and improve biomedical research efficiency. Meanwhile, much more attention will be needed for challenges so as cloud providers will need to meet requirements for data privacy, model interpretability as well as regulatory compliance. The future research should pursue the enhancement of AI explainability, the integration of multi-modal biomedical data, and the improvement of AI driven personalized treatment recommendations. Therefore, this study can contribute to the advancement of AI driven healthcare innovations and help create more accurate and accessible and personalized medical solutions.