Based on the Application and Research of Artificial Intelligence in Medical Diagnosis and Treatment

With the rapid development of artificial intelligence technology, its application in medical diagnosis and treatment is increasingly extensive. In this paper, an intelligent diagnosis system based on convolutional neural network (CNN) and long short-term memory network (LSTM) is proposed to solve the problems of high misdiagnosis rate and low data processing efficiency in traditional medical diagnosis methods. By optimizing the convolutional layer structure of CNN and the hidden layer design of LSTM, the accuracy of the proposed system on the MNIST medical image dataset and MIME-III electronic medical record dataset reached 95.6% and 91.5%, respectively, which was significantly higher than the traditional method (85.4<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 84.7%)[1]. The experimental results show that the system in this paper is superior to the existing studies in accuracy, recall rate and AUC value, especially when dealing with complex cases. In addition, this paper further improves the training efficiency and generalization ability of the model by introducing data enhancement and early stop method. This study provides a new technical means for the application of artificial intelligence in the medical field, which has important theoretical significance and practical application value.