The mutual promotion between healthcare and technology is becoming increasingly close, and artificial intelligence (AI) technology is a hot topic at present. AI technology plays an indispensable role in improving diagnostic accuracy, reducing medical costs, and improving patient experience. The research methods of this study include data collection and preprocessing, model design, model training and validation, and performance evaluation. The data preprocessing steps include data cleaning, conversion, encoding, and segmentation. Model design involves selecting appropriate AI models, Convolutional Neural Network (CNN), Decision Tree (DT), and Support Vector Machine (SVM), and explaining the reasons for the selection. Model training and validation are carried out through strategies such as parameter adjustment and cross validation to optimize model performance. The application of AI technology can enhance the sensitivity of medical services, reduce cost-effectiveness and processing time. The research results indicate that AI models perform well in sensitivity, cost-effectiveness, and processing time, especially in applications with high real-time requirements such as medical image analysis. CNN has a sensitivity of up to 97%, a cost-effectiveness of only £590, and a processing time of up to 14ms. Therefore, AI technology is expected to provide patients with more precise and efficient medical services.