Diagnosis System

Diagnosis systems are rapidly evolving, leveraging machine learning and large language models (LLMs) to improve accuracy and efficiency across various medical domains. Current research emphasizes the development of models using deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as LSTMs), transformer networks, and graph neural networks (GNNs), often combined with techniques like feature extraction (e.g., PCA) and data augmentation to address challenges like imbalanced datasets and limited training data. These advancements aim to automate or assist in the diagnostic process for a wide range of conditions, from cardiovascular diseases and neurological disorders to infectious diseases and even database system malfunctions, ultimately improving patient care and streamlining healthcare workflows. Furthermore, significant attention is being paid to ensuring privacy and security in these systems, particularly when dealing with sensitive patient data.