Artificial intelligence in healthcare

Abstract

The ability of artificial intelligence (AI) to imitate human cognitive capabilities, coupled with the ease of accessibility of medical data and the expeditious advancement of analytical techniques, is bringing paramount difference to the healthcare industry. Former research demonstrates the remarkable accuracy of AI to aid physicians to settle on better clinical choices or supplant judgment made by human beings, in particular, technical and practical areas of medical care. Statistics indicate that the shortage of doctors in therapeutically under-resourced regions and the lack of availability of skilled physicians in highly engaged clinical settings tend to cause a rise in false detection rates. The excessive workload causes fatigue that could lead to poor recovery of diseases. AI is ever-evolving as it is making advancements at an exponential rate, especially in healthcare treatments, such as monitoring treatments, improving the planning, and analyzing data to provide better treatment plans. The procedure of data accumulation, data management, clustering, and tagging invites numerous governance and regulatory challenges that could take a long duration. The healthcare industry thrives in this unending battle as the complexity and intricacy of data, and strict guidelines take a major toll. A healthcare institute is subjected to ask for consent from an institutional review board’s work to attenuate a portion of these concerns, and researchers and scientists may measure, process, and anonymize DICOM information to strip away any patient’s medical information. The AI-enabled medical care employed in the institutes plays an imperative role as an informative assistant that provides aid to doctors in acquiring an understanding of meaningful patterns from data collection. This practice holds the potential to save a lot of time, cost, and effort while yielding consistent, unbiased, and prime diagnosis or treatment. AI and deep learning (DL) tools used in day-to-day medical decision-making have a grave impact on improving the patient’s treatment and overall cost incurred due to their employment efficiency and accuracy. AI and machine learning (ML) can prove to be of supreme importance and assistance in the early detection and thus the prevention of a plethora of diseases by reading and analyzing the patient’s vitals. The presented chapter is of 5-folds. First, the distinct data sources where the healthcare data is gathered from are discussed. Second, we talk about the moral and lawful difficulties of AI-driven medical care. Next, the structured and unstructured types of healthcare data have been analyzed followed by the AI techniques applied to these types of data, such as ML, natural language processing (NLP), and DL.We then analyze the crucial disease areas such as cardiology, radiology, neurology, and cancer, where the health issues can be alleviated by employing AI techniques. In conclusion, we further discuss the areas where AI-based techniques are applied in real life.