Transforming mental health research and care through artificial intelligence

Area of Science:

• Mental Health
• Artificial Intelligence
• Clinical Informatics

Background:

• Mental health care faces unique challenges, including a lack of objective biomarkers and reliance on subjective assessments.
• Stigma and patient privacy are significant considerations in mental health treatment.
• Emerging artificial intelligence (AI) technologies present opportunities to improve mental health care delivery.

Purpose of the Study:

• To explore key domains and applications of AI in mental health.
• To identify challenges hindering the safe, effective, and sustainable clinical integration of AI.
• To propose an approach for AI clinical translation focused on the patient journey.

Main Methods:

• This review synthesizes current research on AI applications in mental health.
• It examines distinctive features of mental health care that impact AI integration.
• The review adopts a patient-centric perspective, considering the entire care pathway.

Main Results:

• AI has potential applications across various mental health domains, from diagnosis to recovery.
• Key challenges include data privacy, algorithmic bias, lack of biomarkers, and overcoming stigma.
• Successful integration requires addressing these challenges within the context of the patient's journey.

Conclusions:

• AI holds transformative potential for mental health care, but careful consideration of challenges is essential.
• A patient-centered approach is vital for the successful clinical translation of AI tools.
• Overcoming barriers related to data, ethics, and stigma will enable AI to enhance mental well-being.