Artificial Intelligence (AI) is the ability of a computer or a robot to perform tasks that are conventionally performed by human intelligence and sophistication (Copeland, 2023).
In its most basic form, artificial intelligence is a field that enables problem-solving by combining computer science and robust datasets (International Business Machines, 2023).
Even though AI originated as a subfield of computer science, its scope of application in other fields have been on a boom. Moreover, the quality and effectiveness of those applications that make use of AI technology have improved (Pannu , 2015) .

According to Alwadi and Lathifa (2022) AI methods and technologies are already in use everywhere we look, albeit frequently in the background. Having said that, the advances in AI are also aiding the mental and behavioural healthcare sectors. In the domains of medical decision-making, testing, diagnosis, and care management, for example, healthcare workers can benefit from computing tools for learning, understanding, and reasoning.  AI technology and approaches may be used to develop self-care solutions that enhance people’s life which further improves the quality of public health by recognising health concerns and guiding interventions. 

AI offers opportunities to enhance various aspects of mental health care, including clinical decision-making, assessment, treatment, self-care, and research.

In the field of healthcare, artificial intelligence (AI) is being used progressively in specialisations consisting of dermatology, radiology, and cancer. However, the implementation of AI in neurobiological research and mental health treatment has been rather restricted. There is a pressing need for AI to assist in identifying individuals at risk and give interventions for both prevention and treatment of psychological ailments due to the high morbidity and mortality in people with psychiatric disorders and an increasing scarcity of mental health care professionals (Lee et al., 2021). Nearly every aspect of behavioural and mental health care, including clinical decision-making, treatments, assessment, self-care, healthcare administration, research, and more, can benefit from the practical uses of AI technology and approaches (Luxton et al., 2015). 

The ongoing development of digital technology and artificial intelligence (AI) has had an influence on the field of mental and psychological care. However, there hasn’t been significant adoption of AI in mental health care (D’Alfonso, 2020). Therefore, it becomes imperative to explore the AI’s integration with therapeutic processes and to investigate the levels of efficiency and effectiveness of this integration. 

This study sought to evaluate the efficacy of integrating AI in the therapy process, from the perspectives of mental health professionals. In order to understand AI and therapy in this context, a search of the literature was conducted. Previous research complementing the present study was considered in the beginning to ensure a thorough understanding of the various elements of the present study and are mentioned below:

In an attempt to learn the contemporary standing of research on AI and mental health,  Feng et al (2022) conducted an extensive review and concluded that the benefits of artificial diagnosis and therapy can be supplemented by the rapidly developing field of artificial intelligence. However, AI still has a lot of space for improvement and currently holds shortcomings that might cause issues, such as algorithm bias, ethical considerations, and difficulties in promotion. As professionals working in the field of mental health, we can actively adapt to it and encourage its further advancement. 

Adding onto it, Brown & Halpern (2021) debates about chatbot replacements wherein three crucially therapeutic components of in-person outpatient mental healthcare are disregarded: 1) The way that mental illness impairs a person’s capacity for motivation and self-advocacy, particularly for those who are socially marginalised; 2) The embodied nature of empathic communication during any clinical encounter that involves attending to complex non-verbal cues; and 3) How social connections provided by in-person clinics provide indirect social benefits that are not part of a clinical checklist. These three issues come with accompanying ethical hazards of failing to treat patients as individuals, to give compassionate care as an act of beneficence, and to provide inclusive care in order to satisfy demands for fairness and justice. 

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