ABSTRACT
Modern medical diagnostics rely heavily on the examination of digital pictures of illness. Artificial intelligence (AI) has been successfully applied to the evaluation of medical images, allowing for accurate evaluations to be performed automatically, thus relieving physicians of some of their workload, reducing diagnostic error, and accelerating disease detection and prediction. Medical image processing-based artificial intelligence approaches are an important topic of study because they use cutting-edge computer algorithms for prediction, diagnosis, and treatment planning, all of which have a substantial effect on decision-making processes. The healthcare industry makes extensive use of modern artificial intelligence methods like machine learning (ML) and deep learning (DL) to aid in illness diagnosis, drug discovery, and risk-factor identification. The development of big data technologies and improvements in electronic medical records have coincided with the rise of ML and DL algorithms in recent years. Neural networks and fuzzy logic algorithms are two examples of ML's algorithmic diversity, with potential uses in the automation of predicting and diagnostics. Unlike traditional neural network algorithms, DL methods may be used without the assistance of a human expert in feature extraction. Medical image analysis, including combination, division, recording, and characterization, shows great promise for DL algorithms with high-performance computations. The most often utilized method for illness analysis and diagnosis is support vector machine (SVM) as an ML approach and convolutional neural network (CNN) as a DL method. The motivation behind this chapter is to give a far-reaching overview of the most up-to-date artificial intelligence tools for diagnosing and forecasting a wide range of illnesses, including cancers, heart, lung, skin, genetic, and brain problems. Also mentioned are the current difficulties and restrictions that AI faces in the medical field.
