Almost one in five adults suffer from a mental disorder at some time in their life.
The shortcomings of the current approaches to treating mental illness create a space where digital tools could be used to close the gap. Currently, it is difficult to provide: a trustworthy and objective diagnosis, timely and useful tracking of health data, individualized treatment plans and continuous psychological support.
Some of these obstacles may be addressed by translating and adapting psychological interventions into digital formats, or digital mental health interventions, or DMHIs.
Data analytics may provide important information and insights to conversations about mental illness. Researchers may be able to better understand mental health disorders and their causes by using it to find patterns and trends in mental health data.
AI has emerged as a promising solution in the field of mental health, offering innovative approaches to diagnosis, monitoring, and therapy. By leveraging machine learning algorithms and natural language processing, AI can analyze vast amounts of data and provide personalized interventions, potentially revolutionizing the way mental illnesses are treated.
Accessibility to these therapies can be increased through the use of digital mental health interventions, such as smartphone applications, virtual and augmented reality, and Internet-based interventions.
More personalized therapies and better mental health services can be developed with the help of data analytics.
It is anticipated that the digitalization of mental health services will bring new approaches to the assessment, monitoring, and study of mental health disorders as well as enhance the quality and accessibility of specialized treatment services.
However, when applying data analytics in mental health research, there are ethical issues as well as privacy concerns that need to be addressed.
AI Technology in Mental Health
AI Technolgy offers potential impact on patient-centered care, long-term treatment evaluation, and improved patient information and decision-making support.
It is possible to objectively and reliably detect or prevent the occurrence of psychological problems through AI-based technologies. Machine learning and deep learning rely on the identification of specific patterns within data sets collected via, for example, psychometric instruments, biomarkers, smartphones, or speech.
The use of digital phenotyping, defined as the “moment-by-moment quantification of the individual-level human phenotype in-situ using data from smartphones and other personal digital devices”, is transforming the field of mental healthcare. Particularly, EMAs seem to be a practical clinical tool that let medical practitioners create a digital phenotype through smartphone apps
Conversational agents, often known as chatbots, are bringing out other significant advances. Chatbots are computer programs that allow text- or voice-based communication with human users through smartphone applications, offering pre-programmed or artificial intelligence (AI) replies. Several studies have demonstrated that their efficacy is on par with in-person treatment, particularly for CBT-based interventions.
The majority of chatbots that have been developed thus far have been utilized for screening, therapy, and training, with an emphasis on autism and depression.
The use of AI in the healthcare industry holds the key to raising the standard of currently provided mental health treatments. To guarantee its effective use and distribution in the clinical and real worlds, more study is necessary.
Further Reading
1. What is the Current and Future Status of Digital Mental Health Interventions?
Published online by Cambridge University Press: 02 February 2022
This article summarizes the main contributions of the different types of digital mental health solutions. It analyzes their limitations (e.g., drop-out rates, lack of engagement, lack of personalization, lack of cultural adaptations) and showcases the latest sophisticated and innovative technological advances under the umbrella of precision medicine (e.g., digital phenotyping, chatbots, or conversational agents).
2. Digitalising mental health care: Practical recommendations from the European Psychiatric Association
Published online by Cambridge University Press: 13 December 2023
The digitalisation of mental health care is expected to improve the accessibility and quality of specialised treatment services and introduce innovative methods to study, assess, and monitor mental health disorders. In this narrative review and practical recommendation of the European Psychiatric Association (EPA), we aim to help healthcare providers and policymakers to navigate this rapidly evolving field.
It provides an overview of the current scientific and implementation status across two major domains of digitalisation: i) digital mental health interventions and ii) digital phenotyping, discuss the potential of each domain to improve the accessibility and outcomes of mental health services, and highlight current challenges faced by researchers, clinicians, and service users.