The integration of Artificial Intelligence (AI) in Healthcare services has emerged as a transformative paradigm, offering innovative solutions to challenges faced by the healthcare industry. With the world facing resource constraints and a burgeoning patient population, AI offers innovative solutions to address these challenges effectively. AI-powered medical imaging systems enable faster and more accurate diagnoses, and personalized patient care strategies. Despite challenges related to data privacy and regulatory frameworks, healthcare sector must embrace responsible AI implementation to leverage its full potential, ultimately leading to improved healthcare accessibility, and increased efficiency in medical services. The integration of AI in healthcare services has the potential to bridge the gaps in healthcare accessibility, and boost the overall efficiency of medical services. This book chapter provides insight into the use of AI in the healthcare sector across the globe, its current status in Healthcare sector, and how it can change the healthcare service.
Top1. Introduction
The rapid industrial revolution has made the space wider for immense advances in technology in terms of growth and production. The replacement of tiring and manual tasks by automated techniques have shaped human life in a different dimension. Artificial intelligence (AI) is defined as the intelligence of machines which is a key technological advancement that has made humans capable of replacing manual work with higher levels of mental capacities and intellectual levels in a variety of industries and organizations (Khang & Rana et al., 2023).
AI is the combination of science and technology through which intelligent computers and computer programs carry out activities that traditionally need human intelligence (Aiken et al., 2000). In AI, “artificial” means objects that are made or created by humans rather than produced naturally and “intelligence” is the ability to formulate strategy to achieve targets with the help of the surrounding that is rich in information. Ideally, AI is made up of a system that contains both software and hardware. The software component deals with algorithms whereas the hardware component works on implementation of those algorithms on a physical computation platform. A key feature of AI is that, it can do different human-like functions, learn from experience and can adjust to new settings and inputs. To perform better on specified task, AI uses relevant sources of information like big data (Kaplan et al., 2020).
AI is a fast-evolving technology with great possibility of making more productive workers, more efficient organizations, opening up avenues for innovations in service and products. In recent years AI has advanced remarkably and its application has brought great change in different industries including finance, education, autonomous driving, agriculture, and biomedical fields specifically the health care industry (Khang & Vladimir et al., 2023).
In the field of agriculture, AI helps farmers to analyze the pattern of weather for weather forecasting, planning their planting schedule, determine the type of crop to grow, manage pest attack etc. In education sector, though human resource is a key component, AI is used to facilitate the automation of digitization of lectures, management of online courses, arranging meeting schedules, sharing personalized information to students and grading homework etc. When it comes to the field of finance, AI has been implemented in every level. Both customers and banks use AI to keep track of accounts, transactions, investments etc (Khang & Abdullayev et al., 2024).
Moreover, AI has risen to prominence in providing support in financial management services. Like all these sectors, with its growing demand day by day, AI also has made its debut in health care sector. There is already a substantial amount of evidence that application of AI can bring tremendous improvement in health care starting from diagnosis of diseases to treatment of patients and AI algorithms outstand the humans in various steps like analysis of medical images, understanding the association of symptoms with patient prognosis (Miller et al., 2018).