Artificial Intelligence in Healthcare: An Overview

*Syed Shahwar Anwar, Usama Ahmad, Mohd Muazzam Khan, Md. Faheem Haider and Juber Akhtar*

#### **Abstract**

The healthcare industry is advancing ahead swiftly. For many healthcare organizations, being able to forecast which treatment techniques are likely to be successful with patients based on their makeup and treatment framework is a big step forward. Artificial intelligence has the potential to help healthcare providers in a variety of ways, including patient care and administrative tasks. The technology aims to mimic human cognitive functions, as it offers numerous advantages over traditional analytics and other clinical decision-making tools. Data becomes more precise and accurate, allowing the healthcare industry to have more insights into the theranostic processes and patient outcomes. This chapter is an overview of the use of artificial intelligence in radiology, cardiology, ophthalmology, and drug discovery process.

**Keywords:** artificial intelligence, algorithms, healthcare, radiology, cardiology, drug discovery

#### **1. Introduction**

In the field of healthcare, Artificial Intelligence (AI) is the privilege to breathe. It's a maneuver of the algorithm for the purpose of diagnosis, prognosis, or treatment of certain diseases. AI is the convergence of human and machine learning. John McCarthy, one of the founding fathers of AI, defined it as "the science and engineering of making intelligent machines" [1]. In this current era, intelligent machines pertain in various domains like financial, automatic driving, smart home, etc. In healthcare, machine learning is widely used to build automated clinical decision systems and in the treatment of different diseases [2]. AI utilizes advanced algorithms to learn from healthcare data and assist healthcare professionals in clinical practice. It has self-correcting and learning capabilities to cope with its exactness based on analysis [3]. AI can detect the spread of endemics by tracking animals and plant diseases and by accessing global airline ticketing data that are when and where the infected residents are moving and detect when an endemic can become a pandemic. The advancement in AI and its caliber to imitate human intelligence is heading towards the passing of Turing test [4] and AI will have a major impact on the forthcoming industrial revolution [5].

#### **1.1 History/evolution**

AI is the ray of computer science that deals with counterfeit intelligent human behavior. Using an electric circuit, Dr. Warren Mculloch and Dr. Walter Pitts [6] described neuronal activity and their modeling and explained the notion of neural networks. AI was first coined at Dartmouth college conference in 1956 [7] and the primitive work of AI was recorded in the 1970s after 15 years of existence of AI. The dendral experiment was the early employment of AI in life science [8]. The interpretation of electrocardiogram (ECG) stepped from 1970 to 1990 is considered as a major development in the field of AI [9]. A clinical decision support system was developed during the 20th century [10, 11]. However, the eagerness about AI was at its peak during the 1980s, but the phenomena of "AI winter" occurred due to groundless forecasting by the observers led to a lack of funding and interest [12]. There was continuous progress in building artificial intelligence in mid-20s by improving the algorithm and feeding the huge data of healthcare and its intelligence makes it cognizance of assisting the clinical cases of patients with the support of healthcare professionals. The renaissance of AI happened in 2012 after the evolution of image classifiers [13] and incorporation of AI in patients treatment needs to be trained on the basis of large data of clinical case studies so that it can examine the patient condition on the basis of history and the data which it has and results in diagnosis and in treatment methodology. The devices which favors in the department of healthcare are trained through machine learning. Basically, machine learning makes computer learn by the provided data i.e. algorithm in great measure [14, 15]. Supervised and unsupervised machine learning are two paradigms of machine learning [16]. Supervised machine learning classified the large data result and separate it into different categories and also predicts the result i.e. regression. In unsupervised machine learning there is no result prediction and it conglomerates the results [17].

The proof of AI and its concept has been demonstrated since older times. The history has been recorded with such minds related to the AI and executed their intellections and till today there are a new development, researches, and inventions going on in the field of AI and its application in different branches of healthcare.
