Artificial intelligence in positive mental health: a narrative review

Summary & key facts

This paper is a broad review of how artificial intelligence, or AI, might help mental health. The authors explain common AI methods and describe ways AI could boost awareness, help with diagnosis, and support treatment for conditions like autism and mood disorders. They also point out important limits, including the risk of biased data, the need for cultural fit, and ethical and privacy concerns.

Key facts:

• The article is a narrative review that surveys the whole area of AI in mental health and explains different AI methods such as machine learning, supervised learning, and unsupervised learning.
• Supervised learning is where a computer learns from examples that include the right answers. Unsupervised learning is where the computer looks for patterns by itself without given answers.
• The reviewers describe possible uses of AI for mental health: raising public awareness, helping clinicians make diagnoses, and supporting interventions or treatments.
• The paper highlights that AI may help with emotional regulation and could affect conditions such as autism spectrum disorders and mood disorders. Emotional regulation means managing or changing how someone feels.
• The review also covers AI work related to neurodegenerative disorders, intellectual disability, and seizures, showing that AI is being explored across many brain and mental health areas.
• The authors warn that AI tools can carry biases from their training data, need to be adapted to different cultures, should use flexible and transparent designs, and bring up ethical and privacy issues.
• The main takeaway is that AI shows promise for improving mental health care, but more careful, culturally aware, and ethical development is needed before those tools can be relied on in everyday care.

Abstract

The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.

Topics

Digital Mental Health Interventions Machine Learning in Healthcare Mental Health Treatment and Access

Categories

Artificial Intelligence Computer Science Physical Sciences

Tags

Anxiety Artificial intelligence Autism Computer science Developmental psychology Emotional intelligence Intervention (counseling) Learning disability Mental health Mood Mood disorders Programming language Psychiatry Psychology Schizophrenia (object-oriented programming) Scope (computer science)

Conditions & symptoms

Anxiety Depression Sadness or low mood