Clinical Practice Guidelines on using artificial intelligence and gadgets for mental health and well-being

Abstract

INTRODUCTION Globally, one in every three individuals suffers from a mental illness during their lifetimes. Low- and middle-income countries (LAMICs) bear 80% of the mental health disease burden. The stigma associated with mental disorders results in delayed help-seeking, reduced access to health services, suboptimal treatment, poor outcomes, and an increased risk of individuals' human rights violations. The actual mental health burden is arguably far higher; assuming the overlap between psychiatric and neurological disorders, mental health disorders (MHDs) account for 32.4% of total years of health lost due to disability (YLDs). Depression, anxiety disorders, bipolar disorder, schizophrenia and other psychoses, dementia, substance use disorders, attention-deficit/hyperactivity disorder, and developmental disorders, including autism, are the leading contributors to mental health morbidity. It is estimated that by 2030, depression will be the third and second highest causes of disease burden in LAMICs and middle-income countries, respectively.[1] Moreover, mental health challenges have increased in recent decades with a rise in suicides, substance use, and loneliness, worsened by the coronavirus disease 2019 (COVID-19) pandemic. Mental health care is compounded by a shortage of mental healthcare professionals, stigma, and lack of facilities. Artificial intelligence (AI) presents a potential solution to address this shortage of mental health professionals, and it is increasingly employed in healthcare fields, such as oncology, radiology, ophthalmology, and dermatology.[2] WHAT IS AI AI is a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior and with the creation of artifacts that exhibit such behavior. AI systems are understood as programs, which enable computers to function in ways that make people seem intelligent.[3] AI functions based on certain principles, which include image processing, computer vision, artificial neural network, machine learning (ML), deep learning (DL), and natural language processing [Table 1]. Among all these, ML is at the heart of AI. ML involves various methods of enabling an algorithm to learn. It is a process of fitting predictive models to data or identifying informative groupings within data. ML attempts to approximate or imitate humans' ability to recognize patterns in an objective manner, using computation.[4] The most common styles of ML used for healthcare purposes include supervised, semi-supervised, unsupervised, Deep learning, and reinforcement learning.[5] A major strength of AI is its rapid pattern analysis of large datasets. AI is used in health care for the early detection of diseases, better understanding of disease progression, optimizing medication and treatment dosages, and uncovering novel treatments.[5] Additionally, intelligent systems are increasingly being used to support clinical decision-making as AI-powered machines can rapidly synthesize information from an unlimited amount of medical information sources. To optimize the potential of AI, very large datasets are ideal (e.g. electronic health records) that can be analyzed computationally, revealing trends and associations regarding human behaviors and patterns that are often hard for humans to extract.Table 1: Principles on which AI functionsAI algorithms can perform as well, or better than experienced clinicians in evaluating images for abnormalities or subtleties undetectable to the human eye (e.g., gender from the retina).[5] Among the different branches of medicine, AI has been most successfully used to leverage pattern recognition in ophthalmology, cancer detection, and radiology.[5] AI IN PSYCHIATRY Mental illnesses pose a heavy burden on society at large. The future of AI in psychiatry appears to have great potential with the growing need and utilization of AI bots in managing psychiatric symptoms and augmenting therapeutic treatments [Table