Overview

Master AI & Machine Learning for 50% Off

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

Enroll Now

Syllabus

Apply safe and effective clinical judgement when using AI tools in medical practice.
Critically evaluate AI-generated documentation and diagnostic outputs.
Recognize cognitive biases and medico-legal risks associated with AI-assisted care.
Communicate AI insights clearly to patients and manage AI-informed consultations.
Document clinical decisions involving AI in a legally defensible manner.
Identify strategies for adapting medical careers and leadership roles in an AI-augmented healthcare system.
Apply AI-assisted documentation and ambient clinical intelligence tools to manage cognitive load, ensure billing defensibility, and maintain high-quality human-authored clinical notes.
Analyse cognitive load in clinical workflows and identify high-burden areas where AI tools offer verified cognitive relief.
Apply ambient clinical intelligence tools using verbal signposting and the 5-step Human-in-the-Loop Audit protocol to produce accurate clinical documentation.
Evaluate AI-assisted documentation against the proportionality principle and apply the four non-negotiables for Medicare-defensible clinical notes.
Evaluate AI-assisted diagnostic outputs for automation bias, interpret AI probability scores using Bayesian reasoning, and implement defensible override documentation that meets medico-legal standards.
Identify automation bias and the five cognitive traps activated by AI in clinical diagnosis, and apply deliberate debiasing strategies.
Apply defensible override note drafting practices that satisfy AHPRA, TGA, and Medicare compliance requirements in AI-assisted clinical environments.
Interpret AI probability outputs using Bayesian reasoning and communicate AI-derived findings to patients in accessible, clinically appropriate language.
Analyse the impact of AI disruption across medical specialties and apply career-stage-appropriate strategies to mitigate deskilling risks and contribute to AI governance decisions.
Distinguish genuine AI disruption from capability overstatement using a task-decomposition framework to classify automatable, augmentable, and irreducibly human clinical tasks.
Apply emotional intelligence principles to navigate AI-altered patient consultations, maintain therapeutic trust, and mitigate patient safety risks in AI-augmented clinical interactions.
Evaluate specialty-specific AI disruption risks and apply strategies to protect training integrity against deskilling, mis-skilling, and never-skilling threats.
Design a career-stage-appropriate AI adaptation strategy and identify physician leadership roles in AI governance, procurement, and clinical validation.
Identify why emotional intelligence becomes more clinically critical as AI expands in healthcare settings.
Apply emotional intelligence principles to navigate AI-altered patient consultations, maintain therapeutic trust, and mitigate patient safety risks in AI-augmented clinical interactions.