Augmenting Mental Healthcare With Artificial Intelligence, Machine Learning, and Challenges in Telemedicine

1 Introduction

The healthcare industries are nowadays dominated by Artificial Intelligence and other technologies. With increased dependency on computing devices, the Internet of Things, digital data, AI applications have entered all parts of the biomedical world. We have entered industry 4.0 known as the “digital revolution” characterized by a fusion of technology types (Pang et al., 2018; Schwab, 2017). Although many influential segments of society are ready to accept AI's potential, medicine, including psychiatry, remains cautious, as indicated by recent reports in the news media such as “A.I. Can Be a Boon to Medicine That Could Easily Go Rogue” (Simon, 1991). Despite obvious reservations, AI's use in medicine is gradually growing. The rising incidence of health issues and costs in the aging population in the current pandemic is putting strain on the healthcare system. New technology, such as artificial intelligence (AI), may improve outpatient and inpatient treatment, emergency services, and preventative care in health care facilities.

Artificial intelligence (AI) and related breakthroughs are gaining traction in business and culture, and they're starting to make their way into healthcare. Many aspects of patient care, as well as administrative operations within providers, payers, and pharmaceutical companies, benefit from these improvements.

This chapter provides an overview of AI in healthcare, a description of recent studies on AI and mental health (methods/results), and a discussion of how AI might supplement mental health clinical practice while taking into account existing flaws, areas that need more research, and ethical concerns. Scientists are now developing a speech-based mobile software that can compartmentalize a patient's mental health status better than a professional can do.

Further, this chapter discusses the scientific, technical, and theoretical basis of automatic mental state detection systems. It gives us some descriptive examples of systems capable of automatically detecting a range of mental states that are predominately pertinent to mental health care (e.g., stress, depression, pain). The chapter winds up by looking at the existing condition of artificial intelligence and focuses on the key challenges that need to be addressed before these health care systems can get benefit from its widespread use. In the upcoming years, AI will have a meaningful impact on people’s lives by improving their mental health is proper laws are framed in the country.