Overview

Master AI & Machine Learning for 50% Off

Go under the hood of AI — neural networks, real-world applications & more. Designed by UNSW experts.

Gain advanced, practice-ready skills for integrating artificial intelligence into mental health clinical settings through this professional development course designed for mental health practitioners and students. Master five evidence-based frameworks — VERIFY-MH, ALLIANCE-AI, SAFE-MH, ETHICS-MH, and clinical governance principles — to critically evaluate, safely apply, and ethically deploy AI tools across a range of psychiatric and therapeutic contexts. Learn to critically appraise AI-generated diagnostic suggestions and document AI-assisted psychiatric assessments to a four-element medico-legal standard. Assess whether a patient's use of AI chatbots or companion apps poses therapeutic risk, and navigate the consent, confidentiality, and documentation obligations that arise when AI scribes are used in therapy sessions. Apply structured clinical risk assessment independently of AI-generated risk scores, and document both AI outputs and your own clinical reasoning to meet medico-legal requirements. Evaluate AI tools against ethical standards before deploying them in practice, and draft AI-specific consent statements that satisfy therapeutic, ethical, and Australian Privacy Act obligations. Verify AI-generated MBS billing codes for mental health consultations, identify patient safety risks in automated triage systems, and apply clinical governance principles to AI platforms used across a mental health practice. Demonstrate integrated competency across all five frameworks through six sequential branching clinical encounters in a simulated mental health practice scenario, with a minimum 70% threshold required to qualify for the Certificate of Completion. Develop the confidence to apply these frameworks in supervised clinical placement contexts, navigate common AI-related professional pitfalls with integrity, and articulate AI literacy competencies clearly in graduate mental health interviews.

Syllabus

Apply the VERIFY-MH framework to critically evaluate AI-generated diagnostic suggestions before incorporating them into clinical formulations, and document AI-assisted psychiatric assessment to a 4-element medico-legal standard.
Apply the ALLIANCE-AI framework to assess whether a patient's chatbot or companion use poses a therapeutic risk, and implement the consent, confidentiality, and documentation obligations specific to AI scribes used in therapy sessions.
Apply the SAFE-MH framework to AI-generated risk scores, conduct an independent structured clinical risk assessment regardless of AI output, and document both the AI score and clinical reasoning to a medico-legal standard.
Apply the ETHICS-MH framework to evaluate an AI tool before deploying it in mental health practice, and draft an AI-specific consent statement that meets therapeutic, ethical, and Australian Privacy Act obligations.
Verify AI-generated MBS billing codes for mental health consultations before submission, identify patient safety risks in automated triage, and apply clinical governance principles to AI platforms used across the mental health practice.
Demonstrate integrated application of all five clinical frameworks across six sequential branching clinical encounters in a mental health practice scenario, achieving a minimum 70% threshold to qualify for the Certificate of Completion.
Apply VERIFY-MH, SAFE-MH, ALLIANCE-AI, and ETHICS-MH in supervised clinical placement contexts, navigate graduate AI traps with professional courage, and articulate AI literacy competencies confidently in a graduate mental health interview.