Abstract PO-056: Importance of artificial intelligence, machine learning deep learning in the field of medicine on the future role of the physician

Abstract

Abstract There are many ways to define the field of Artificial Intelligence. Here is one way for Artificial Intelligence is "The Study of the computations that make it possible to perceive, reason, act and predict the future possible outcomes”. Deep learning, which is a popular research area of artificial intelligence (AI), enables the creation of end-to-end models to achieve promised results using input data. Deep learning techniques have been successfully applied in many problems such as arrhythmia detection, skin cancer classification, breast cancer detection, brain disease classification, pneumonia detection, COVID-19 from chest X-ray images, and CT scan images. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of diseases. However, the chest CT classification involves a radiology expert and considerable time, which is valuable when any infection is growing at a rapid rate. Therefore, automated analysis of chest CT images is desirable to save the medical professionals precious time that shows the importance of Artificial Intelligence neural networks which is used to classify the infected patients as infected (+ve) or not (−ve). There is a vital need to detect the disease at an early stage and save the patient from the disease. Convolutional neural networks (CNN) are a powerful tool that comes under the platform of Neural Networks – Artificial Intelligence inspired by the human brain, which is extensively utilized for image classification. The hierarchical structure and efficient feature extraction characteristics from an image make CNN a dynamic model for image classification. Initially, the layers are organized into three dimensions: width, height, and depth. The neurons in a given layer do not attach to the entire set of neurons in the later layer, but only to limited neurons of it. Finally, the output is diminished to a single vector of probability scores, coordinated alongside the depth dimension. In a Convolutional Neural Network, the linear function that is used is called a convolutional layer. Each node in the hidden layer extracts different features by using image processing feature detectors. For example, in the first layer, the first node may extract the horizontal edges of an image, the second node may extract vertical edges and etc. These features are extracted using a kernel. The bottom is the original image and the top is the output of the convolutions. It is also worth noting that the output of the convolutions reduces the dimension of the original image, The next step the pooling layer happens tends to be computed after the convolutional layer. The reason why pooling is done is to further reduce the dimensions of the convolutional layer and just extract out the features to make the model more robust. AI could help to rapidly diagnose diseases if proper attention given in collecting the data. Citation Format: Subash Kumar. Importance of artificial intelligence, machine learning deep learning in the field of medicine on the future role of the physician [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-056.