Chapter 9 SOK: Application of machine learning models in child and youth mental health decision-making

Artificial intelligence (AI), specifically machine learning, has the potential to augment human decision-making in mental healthcare to improve clinical outcomes. An important challenge is the knowledge gap between AI designers and mental health professionals. We review AI researchers’ and health professionals’ related terminologies, concepts, and perspectives to facilitate interdisciplinary collaborations in AI-assisted decision-making research. We present a case study to understand the decision-making processes at McMaster Children's Hospital's Child and Youth Mental Health Outpatient Services Program. We identified three decision points that can be augmented using AI: diagnosis, prognosis, and assessment. We present a systematic literature review of forty publications using the PRISMA methodology to investigate AI in mental health. Most publications focused on developing classification and prediction models outside the clinical workflow. We report open research challenges investigating how health professionals’ decision-making changes to AI, where AI can assist decision-making, and how the human–AI feedback loop informs model improvement.