If you are in distress, you can text WELLNESS to 741741 at any time. If it is an emergency, call 9-1-1 or go to your local emergency department.

Home › Resources › Artificial Intelligence in Mental Health Services: Results From a Literature Review and an Environmental Scan

Artificial Intelligence in Mental Health Services: Results From a Literature Review and an Environmental Scan

Artificial intelligence (AI) is increasingly being used in health care, where it has the potential to augment care, change how it is delivered, and improve access. For mental health care, AI applications are being developed for the prevention, detection, diagnosis, and treatment of mental health problems or illnesses.

With the high demand for mental health services in Canada, these technologies could have an important role in providing mental health care. Uncertainty remains, however, about their effectiveness and appropriate use.

AI in mental health care

With most AI applications still in research and development, their clinical use for mental health care is limited. Available clinical uses include diagnosis (determining the presence of mental illness) and prevention (e.g., detecting risk, and connecting users to appropriate supports) as well as treatment (e.g., using conversational agents to deliver cognitive behavioural therapy).

Key research in AI for mental health

Most research and development initiatives are aimed at diagnosis (e.g., the use of patient data to detect or diagnose mental illness), while some research focuses on prevention and prognosis (predicting a client’s response to treatment). In addition, the analysis of social media posts ( e.g., Twitter, Facebook) has been explored as a way to support early detection and diagnosis for people experiencing mental health illness including major depression, anxiety, and postpartum depression. Recent trends in AI research and
development show an increased interest in the use of AI applications for wearable devices and smartphone-based sensors to collect data.

What to consider when using AI in mental health care

Decision-makers should ensure that the AI intervention will translate well from a lab environment to clinical use. This may require
careful planning to ensure that:

  • ethical requirements are met
  • the technology is suitable and effective for
  • mental health care
  • clinician and client perspectives are considered
  • culturally sensitive algorithms are created.

Note:
The information in this report is based on the technologies available when these reviews were conducted.

For more information, read the Environmental Scan in Artificial Intelligence (AI) and Literature Review in Artificial Intelligence (AI).

Feedback Form

Hey, there! Thanks for checking out this resource. We’d love it if you could share a little more info about yourself and how you got here (What kind of information were you looking for? Did this resource help?). Doing so will help us create better content in the future. Thanks!

Disclaimer:

  • The completion of the form is voluntary.
  • The information collected will be used solely and exclusively by the Mental Health Commission of Canada to improve the quality of our documents.
Are you willing to be contacted within 3 to 6 months for a short follow-up survey?
In case of “Yes” – please provide an email address

Disclaimer

Your feedback will only be used for feedback purposes. Thank-you for participating in our feedback program.

This field is for validation purposes and should be left unchanged.
RELATED
Common mental health problems and illnesses The most common mental health problems and illnesses among those being treated for cancer and cancer survivors. How do mental health and cancer interact?...
What are chronic diseases? Chronic (or non‑communicable) diseases last for at least three months, are not passed between people, and progress over time. What are mental health problems or illnesses?...
Background Physical and mental health co-morbidities are common, however, little is known about their prevalence, incidence, associated healthcare-related costs, shared etiology, prevention and management. A better understanding of how to...