A surprising statistic regarding the rise of AI in mental health care is that over 45% of individuals open to trying AI therapy tools are over the age of 45. This challenges the assumption that only younger generations are comfortable using AI for mental health support. It suggests a growing acceptance of AI therapy among older demographics who might have previously been hesitant to utilize technology for mental health needs.
Key points about this statistic:
· Breaking age barriers:
This statistic indicates that concerns about technology literacy among older adults may not be as significant as previously thought regarding AI-based mental health interventions.
· Accessibility and privacy:
Older adults might be attracted to AI therapy due to its convenience, anonymity, and ability to access support without needing to travel to appointments.
· Potential for wider adoption:
This trend could signal a significant increase in the overall use of AI therapy tools as more people from diverse age groups become comfortable with the technology.
The latest technologies, such as artificial intelligence (AI), have improved healthcare delivery significantly in different settings.7,8 Even though AI technology can be traced back to the mid-20th century, recent decades have witnessed significant growth in its usage in healthcare.9,10 AI in the healthcare sector decreases the workload of healthcare workers, makes a quicker diagnosis, and leads to efficient patient care, including care for mental health. It is estimated that the involvement of AI in healthcare in the USA may contribute to a decrease in healthcare expenditure by 150 billion USD by 2026.11 Globally, mental healthcare faces numerous challenges, including lack of access to mental healthcare services, lack of mental healthcare professionals, fragmentation of care, resource constraints, and lack of funding.12,13 According to the American Psychological Association’s new report, the need for mental healthcare professionals is on the rise, and they are unable to cater the service to new patients. This scenario is further worsened among the marginalized communities. Additionally, they stated that with the shortage of mental health professionals, digital therapies may be crucial in providing care for underserved populations.14 Over the period of time, from the first AI in mental healthcare, ELIZA, developed by Joseph Weizenbaum in 1966, there have been tremendous developments in AI technologies in mental healthcare, especially in the past decades. The recent development includes AI usage in almost all aspects of mental healthcare.15–17 Hence, mental healthcare, which is currently in the health sector transformation era with emerging digital technologies such as AI, is reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses.18 Machine learning (ML) and deep learning (DL) are AI subcategories that try to solve challenges related to mental health care. Mobile apps, virtual clinics, digital screening, relaxation apps, and digital treatments are some of the technologies often utilized in mental healthcare that garnered attention and were proven effective during and after the COVID-19 epidemic.19,20 Nonetheless, AI applications in mental healthcare possess their own demerits, such as privacy concerns, a lack of human interactions, and regulatory challenges. These concerns are making mental healthcare providers reluctant to use or refer their patients to AI. Hence, it is essential to understand current trends and gaps in knowledge of AI integration in mental healthcare. Therefore, policymakers can gain insight into the importance, cost-effectiveness, patients’ expectations, and outcome of this critical public health issue. Considering the aspects mentioned above, the present review is aimed at discussing the updated landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, we attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. Finally, this narrative review identified existing knowledge gaps that give future research directions on AI in mental healthcare.
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