Artificial intelligence in mental health care

Abstract

Mental health is one of the most important yet often overlooked aspects of our well-being. Due to the shortage of mental health specialists and the rising incidence of mental health problems, many people have difficulty getting the mental health care they require. Hence, there is a growing need for more effective, affordable, and accessible forms of mental health support. This is where artificial intelligence (AI) comes in. This technology is changing how we think about mental health and offers new hope to those struggling. In this article, we will explore AI's application, challenges, and future concerns in mental health. WHAT IS ARTIFICIAL INTELLIGENCE? According to the English Oxford Living Dictionary, AI is “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”[1] There are two types of AI. (i) Narrow (Weak) AI can perform only a limited set of predetermined functions, e.g. Apple's Siri, Amazon's Alexa. (ii) General (Strong) AI is considered to match the human mind's capacity for independent thought because of its capability to process a wide range of inputs.[2] In 1950, Alan Turing introduced the Turing test to determine if a computer could demonstrate the same intelligence as a human. In 1956, John McCarthy coined the term “Artificial Intelligence” at the first-ever AI conference at Dartmouth College.[3] Since then, scientists have been trying to develop AI models that can be applied to many sectors and industries, such as automotive, finance, and health care. The most common AI-based technologies used in health care include chatbots, virtual reality therapy, and machine learning algorithms. APPLICATION IN MENTAL HEALTHCARE Screening AI helps in understanding mental illness,[4] and it is used for the screening of severe mental illness[5] with clinical magnetic resonance imaging scans,[6] bipolar disorder,[5] depression in old age[4,7] Alzheimer's, mild cognitive impairment, autism spectrum disorder, obsessive–compulsive disorder, and posttraumatic stress disorder (PTSD).[5] AI can examine data from various sources, including social media, to find patterns of activity that might be related to mental health problems. This data helps to identify the high-risk individuals;[8] predict depressive relapses of bipolar disorder.[9] However, recent studies found that AI models have variable performance in diagnosing mental illness.[8] Therapies Patients can now receive cognitive behavioral therapy treatment from AI-powered virtual therapists such as Woebot.[10] Patients can communicate with these virtual therapists by voice, video, or chat anytime.[11] Similarly, AI models are tested to deliver micro-intervention for parents[4] and also significantly reduce the symptoms of anxiety and depression during the COVID-19 pandemic.[12] AI-based virtual reality therapy allows patients to experience and confront their fears in a controlled and safe environment. It is widely used for patients with anxiety, phobia, and PTSD.[13] Further, industrial AI efficiently enhances workers' mental health and addresses a variety of mental health concerns.[14,15] Speech analysis technology uses machine learning algorithms to analyze speech patterns and identify emotional states. This technology helps to identify patients who are at risk of depression or anxiety and also be used to monitor the effectiveness of therapy.[16] CHALLENGES Because mental illnesses are highly subjective, have complicated symptoms, differ from person to person, and have strong sociocultural linkages, their diagnosis requires thorough investigation.[17] Joyce et al. argue that the mental illness etiology, signs and symptoms, and outcomes are highly interrelated. Further, the determinants of mental illness are multi-factorial, i.e. biological, social, and psychological. Hence, the AI models' understandability of mental illness needs to be heightened.[18] Suppose an AI model is prepared with responses from unauthentic data. In that case, it can offer false information about the illness and improper guidance, possibly harmful to persons with mental illness.[19,20] The difficulties that AI in mental health must overcome before it can contribute to a strong base as a support tool in mental health management are indicated by the absence of information to ensure reproducibility and transparency.[21] Based on nonrepresentative samples, there is a possibility of creating biased models. Older adults have been demonstrated to be capable of learning and using tools with specialized programs; however, they are at significant risk of being excluded from AI studies due to their limited access to and familiarity with technologies.[22] There are no established guidelines for the appropriate use of data standards and nursing terminologies. Nursing documentation must be consistent even within a facility adopting standard nursing terminology. Nursing data cannot be routinely used for quality measurement or improvement because of poor recordkeeping and a lack of consensus on standards.[23] FUTURE Earning the trust and confidence of clinicians should be the foremost consideration in implementing any AI-based decision support system.[24] While AI developers are keen to concentrate on person-like solutions, partnerships with mental health professionals are necessary to ensure a person-centered approach to future mental health care is required.[25] Studies emphasize the value of contextualizing interventions and recommend that scalable and evidence-based mental health care be available to large populations through AIs.[12] App Advisor is a creative project of the App Evaluation Model that the American Psychiatric Association developed to evaluate applications for their effectiveness, acceptability, safety, and capacity to provide mental health care.[20] At the same time, strong laws are needed to protect individuals or groups from harm by accessing, disclosing, or manipulating mental health data.[26,27] AI cannot accurately diagnose mental illnesses, so it cannot replace clinicians' diagnoses soon. The underlying difficulty in diagnosing mental illnesses using AI is not technological or entirely data-related but rather our general understanding of mental illness.[17] Many years of therapeutic transcripts are required to offer an inexpensive tool capable of delivering complex and tailored therapeutic models with high fidelity, compassion, and perfect recall that can simultaneously engage thousands of clients.[28] CONCLUSION AI is paving the way for more personalized, efficient, and effective mental health care. To realize AI's potential while reducing the possible harm, substantial effort should go toward the careful and thoughtful introduction of these AI technologies into global mental health. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.