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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.

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