Advancing Precision Drug Screening: Integrating Imaging Technology and Artificial Intelligence for Novel Models

This article discusses in detail the application of combined imaging technology and artificial intelligence in precision drug screening. In view of the efficiency and accuracy challenges faced by traditional drug screening methods, such as high-throughput screening, this study proposes a new screening mode. This model combines advanced imaging techniques (such as MRI and CT) with deep learning models, especially convolutional neural networks (CNN), to improve the efficiency and accuracy of drug screening. Combining intuitive drug action information obtained through imaging technology with the advantages of artificial intelligence in processing complex big data, this study aims to improve existing drug screening processes. The experimental part includes preprocessing of imaging data, model training and optimization, and comprehensive evaluation of model performance. Model performance was evaluated in detail through a range of metrics such as accuracy, recall, precision, and F1 score. This study shows that a method combining imaging technology and artificial intelligence can significantly improve the accuracy and efficiency of drug screening, which has important implications for future drug discovery and personalized treatment.