ABSTRACT
Artificial intelligence (AI) is playing a significant role in revolutionizing health-care industry. With the rapid increase in availability of new clinical data sources and with the evolution of new AI-based technologies, its clinical applications are significantly increasing. AI in medicine and medical domain is helping administrators, practitioners, patients and other stakeholders by emulating the intelligent behavior of human in computers and in other specialized machines that are being used in many promising medical care applications. The availability of large standard datasets is crucial for the training of these AI-based systems. A variety of medical data sources specifically used in diagnosis process exist such as (1) numeric and textual data – that basically includes patient's attributes, i.e. gender, age, clinical history, disease symptoms and physical examination results, that are mostly used for risk prediction of particular disease, textual reports, i.e. physical examination outcomes, operative notes, laboratory reports and discharge summaries; (2) images data – that encompasses screened images obtained from different modalities such as radiology images (i.e. X-rays, computed tomography (CT) scans, magnetic resonance imaging (MRI)), dermoscopy images, fundus and eye screening images, pathology images and many more; (3) sound data – such as ultrasound (US), heart sound signals that are used for early diagnoses of disease and analysis of internal human body parts; (4) genetic data – used for the diagnosis of several complex diseases such as cancers, Down's syndrome and infectious disease. The successful applications of AI in healthcare attracted many small and large companies to invest in this domain. Such companies include Amazon, Microsoft, Google and Nvidia.
Broad categories of AI methods are being employed by researchers to provide better solutions in medical domain. These methods mainly include (1) knowledge-based expert systems (ES), which are primarily based upon a series of if-then rules defined by the domain experts, specifically utilized in clinical decision support systems; (2) machine learning (ML), in which statistical models are trained over different clinical datasets to perform tasks like disease detection, treatment planning and disease prediction; (3) deep learning (DL), which is state-of-the-art auto learning mechanism based on multilayer perceptron and their enhanced forms that can assists in risk prediction, prognosis, detection and diagnosis of different disease etc. by recognizing patterns in training data. There are numerous application areas where AI methods can be used to improve the performance of systems and clinical results, which may include (1) computer vision, which assists in detection and diagnosis of disease and in monitoring of patients using medical images like X-rays, MRIs and US; (2) natural language processing (NLP), which assists in creation and interpretation of patient's medical reports using structured and unstructured medical and contextual data; (3) wearable devices, which help in patient's monitoring (i.e. observe patient's health condition) by recording different biomedical signals (i.e. blood pressure, heartbeat); and (4) virtual assistants, which are autonomous entities that can formulate their decisions based on their interactions with their environment and their self-learning mechanism. These are the systems that can operate in the absence of human intelligence and may range from simple systems like thermostat to complex networked systems like army of robots.
In addition to these, AI also assists in the production of new medicines, in prediction of drug-drug interaction, and helps patients using virtual assistants or chatbots, backings doctors in patient monitoring using Web-based or cloud-based systems, contributing in administrative tasks etc. Among all of these wonderful applications of AI, automated disease diagnosis is the major and the most successful domain. Providing the right and optimal diagnosis at the right time is an important aspect of medical care industry. Normally, radiologists and other clinicians perform manual scrutiny of these screened images to find different abnormalities, which requires a large amount of time and may prone to subjectivity. Moreover, the chance of errors could be further increased due to lack of expert's experience, visual fatigue, physical health issues of professionals etc. One of the potential solutions to this problem is intervention of computerized AI-based systems in healthcare to reduce these medication errors.
This chapter presents an extensive study of some AI technologies and their applications in the detection, diagnosis, disease treatment, prediction and prescription of different human diseases. Our main goal here is to draw a bigger picture and establish the context for the remainder of this book. The chapter is structured as follows: we will first explore different types of medical data and their use in AI-based healthcare. After that, we will introduce some AI technologies and its applications used in medical care systems. We will then learn how these AI technologies and medical data are being used by AI systems for detection, prediction and diagnosis of different types of disease. At the end of this chapter, we will cover benefits and challenges of using those AI systems in medical care.
