AI- POWERED DIAGNOSIS: REVOLUTIONIZING HEALTHCARE WITH NEURAL NETWORKS

This paper discusses the development and evaluation of an AI-powered diagnostic system that utilizes deep learning techniques, such as neural networks, to improve the accuracy of diagnosis in healthcare. The proposed method includes several stages: data collection, preprocessing, neural network training, diagnosis generation, and result evaluation. Medical data, including imaging, patient history, and diagnostic reports, are gathered from diverse sources to ensure comprehensive input for the system. Preprocessing techniques involved normalization, transformation, and augmentation of the raw data to prepare them for training. The labeled datasets trained a neural network, in this case a Convolutional Neural Network (CNN) for images, to learn patterns regarding diseases. When the system is trained, it diagnoses new patients with decision thresholding to be applied at the output level to give high confidence on predictions. This comparison of the system with human doctors proves to be more rigorous, with higher accuracy, precision, recall, and F1-score. Moreover, it infers faster and even in the presence of noisy or missing data, exhibits robust performance. The outcomes demonstrate that AI models may have a great potential in enhancing the efficiency and accuracy of diagnostics in healthcare, which positions them as an asset in clinical decision-making.