Here are some recommendations on how to enhance with AI the delivery of psychological assessment and mental health screening, informed by a set of evidence-informed articles (Click on the button below for a shared folder of articles.)
Apply AI for automated scoring of self-report scales or open-ended responses, but always review results manually and interpret them within the broader clinical context.
Employ AI-generated summaries to highlight patterns across multiple assessments, helping you track trends and present a clearer narrative to clients and families.
Integrate natural language processing to transcribe, organize, and preliminarily code qualitative interview data, speeding up thematic analysis for diagnostic clarity.
Leverage AI to personalize follow-up screening, suggesting targeted questionnaires based on initial responses, while maintaining human oversight to avoid bias.
Use AI-supported personality profiling tools carefully to complement, not replace, traditional personality measures and interviews.
Apply AI tools to support collaborative, therapeutic assessment — for example, co-creating assessment questions with the client to increase engagement and clarity.
Continuously monitor AI scoring tools for cultural validity, updating models and human oversight protocols to ensure validity across diverse client populations.
Follow published AI scoring guidelines: always disclose to clients when AI is used, verify scoring accuracy, and avoid scoring AI-generated or AI-edited client responses (per Dumas et al., 2025; review article on the left).