Abstract
Artificial intelligence (AI) is assuming an increasingly significant role in our daily lives, primarily due to the array of benefits it offers when applied. These advantages encompass round-the-clock availability, an exceptionally low error rate, the capacity to deliver real-time insights, and swift data analysis. In the domain of clinical medical and dental healthcare, AI is experiencing growing utilization with noteworthy applications that encompass disease diagnosis, risk assessment, treatment planning, and drug discovery. This chapter undertakes a comprehensive analysis of AI's integration into healthcare from a multidisciplinary perspective, with a specific focus on the healthcare sector.
TopIntroduction
A physician scarcity and growing healthcare costs are driving a revolution in the healthcare industry right now [Datta et al., 2019]. [Bajwa and others, 2021]. To address these issues, a number of industries are adopting state-of-the-art Information and Communication Technology (ICT)-based procedures and solutions, which have the potential to lower expenses and provide efficient healthcare solutions for the changing environment [Hu et al., 2021]. Since its invention in the 1950s, artificial intelligence (AI) has grown rapidly in the field of modern healthcare [Barua et al., 2023]. It has an impact on almost every aspect of healthcare, including patient self-management of chronic illnesses at home and professional decision support at the point of treatment. AI in healthcare improves illness diagnosis, treatment, and prediction, which is advantageous to patients as well as medical personnel [Amisha et al., 2019]. [Hashimoto & Associates, 2020]. Artificial intelligence (AI) can mimic the diagnostic abilities of human doctors by rapidly analysing patients' electronic health information on both horizontal and vertical axes, which improves diagnosis accuracy [Barua et al., 2023]. AI systems help minimise hospitalisations, minimise problems, cut administrative expenses, and improve patient involvement. They are usually powered by data feeds and sophisticated algorithms. With the use of AI systems, this shift from a disease-based system to one that prioritises prevention and health management is expected to quicken and broaden [Fanelli et al., 2020].
Figure 1. Artificial intelligence applications in a range of healthcare domains
Key Terms in this Chapter
Biomedical Engineering: An interdisciplinary field that combines principles of engineering and biological sciences to develop innovative solutions for healthcare and medical applications. It involves the design, development, and improvement of medical devices, diagnostic tools, and therapeutic techniques, with the aim of enhancing patient care and advancing medical science.
Robotics: A multidisciplinary field focused on the design, creation, and utilization of robots. Robots are autonomous or semi-autonomous machines that can carry out tasks in various environments. Robotics combines engineering, computer science, and other sciences to develop machines capable of physical interaction, automation, and human-assistance across numerous industries.
Health Care: Healthcare encompasses a broad range of services and practices focused on maintaining and improving individuals' physical and mental well-being. It includes medical diagnosis, treatment, prevention, and care services delivered by healthcare professionals and institutions. The goal of healthcare is to promote health, treat illnesses, and enhance overall quality of life.
Artificial Intelligence (AI): AI, or Artificial Intelligence, refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human reasoning, learning, and problem-solving. AI technologies include machine learning, natural language processing, and computer vision, allowing systems to analyze data, make decisions, and adapt to changing circumstances.
Machine Learning: A subset of artificial intelligence that enables computer systems to learn from data and make predictions or decisions without being explicitly programmed. It involves the development of algorithms that analyze and interpret data, identify patterns, and improve their performance over time through experience, facilitating autonomous problem-solving and decision-making.