Research on Disease Assisted Diagnosis Decision making Based on Deep Learning

Disease diagnosis is the core link in the medical process, and accurate diagnosis is crucial for the treatment and prognosis of patients. Traditional disease diagnosis methods mainly rely on the comprehensive judgment of doctors’ clinical experience and professional knowledge, but this method may face limitations in the diagnosis of some complex diseases. In recent years, deep learning, as an important branch of artificial intelligence, has achieved great success in many fields such as image recognition and speech recognition. Applying it to the field of disease diagnosis decision-making is expected to provide new ideas and methods for medical diagnosis, improving the accuracy and efficiency of diagnosis. This study introduces the basic concepts and related technologies of deep learning, as well as its advantages. It then elaborates on the importance and specific methods of data collection and preprocessing, analyzes the process of building deep learning models, and uses various diseases as examples to demonstrate the application results of deep learning models in disease diagnosis decision-making. It is hoped that this study will provide reference for promoting research and clinical practice in disease assisted diagnosis decision-making.