A Review on Patient Monitoring and Diagnosis Assistance by Artificial Intelligence Tools

Abstract

In today’s world, digitalization is becoming more popular in all aspects. One of the keen developments that have taken place in the 21st century in the field of computer science is artificial intelligence (AI). AI has a wide range of applications in the medical field. Imaging, on the other hand, has become an indispensable component of several fields in medicine, biomedical applications, biotechnology, and laboratory research by which images are processed and analyzed. Segmentation of images can be analyzed using both fuzzy logic and AI. As a result, AI and imaging, the tools and techniques of AI are useful for solving many biomedical problems and using a computer-based equipped hardware–software application for understanding images, researchers, and clinicians, thereby enhancing their ability to study, diagnose, monitor, understand, and treat medical disorders. 196Patient monitoring by automated data collection has created new challenges in the health sector for extracting information from raw data. For interpretation of data quickly and accurately the use of AI tools and statistical methods are required, for interpretation of high-frequency physiologic data. Technology supporting human motion analysis has advanced dramatically and yet its clinical application has not grown at the same pace. The issue of its clinical value is related to the length of time it takes to perform interpretation, cost, and the quality of the interpretation. Techniques from AI such as neural networks and knowledge-based systems can help overcome these limitations. In this chapter, we will discuss the key approaches using AI for biomedical applications and their wide range of applications in the medical field using AI. The different segmentation approaches their advantages and disadvantages. A short review of prognostic models and the usage of prognostic scores quantify the severity or intensity of diseases.