Overview

Master AI & Machine Learning for 50% Off

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

This advanced course develops nurses’ and midwives’ ability to use, evaluate, document, and govern AI while preserving independent clinical reasoning and patient safety. It addresses AI-related roles, algorithmic bias, clinical monitoring, medication safety, ethical risks, and professional development across career stages.

Syllabus

Apply the Student Independent Assessment Protocol (SIAP) to form and document independent clinical hypotheses before consulting AI outputs, and critically evaluate the impact of early AI dependency on student skill development.
Apply a structured student escalation protocol when AI outputs conflict with clinical observation and evaluate appropriate documentation standards for AI-augmented placement portfolios.
Evaluate how AI substitution in clinical academic tasks produces reasoning poverty in graduating nurses and midwives, and apply a level-specific AI use framework to preserve scholarly and clinical development.
Design a level-specific career development strategy for an AI-transformed healthcare workforce and evaluate emerging AI-related nursing and midwifery roles including clinical informatics, AI governance, and digital health education pathways.
Apply the Independent First Protocol and 5-step documentation audit to maintain independent clinical reasoning and produce legally defensible records in AI-augmented ward environments.
Evaluate AI monitoring systems using the 5-question evidence evaluation framework and apply active human oversight strategies to manage alert fatigue and patient deterioration risk.
Apply the Nursing Advocacy Protocol for algorithmic bias and evaluate midwifery-specific AI risks in intrapartum monitoring, reproductive decision-making, and postpartum care to protect patient rights and cultural safety.
Design a nurse-led AI governance engagement strategy and apply a career-stage framework to position AI literacy as a professional competitive advantage from new graduate through to nursing leadership roles.
Critically evaluate the ethical risks of AI deployment in community, disability, Indigenous, and migrant health contexts, and formulate a personal action plan for becoming an ethically discerning user of AI who can override algorithmic recommendations based on clinical intuition, cultural context, and recipient wishes.
Apply the Medication AI Safety Principle to maintain independent pharmacological reasoning across AI-assisted prescribing, administration, and monitoring, while articulating student-specific and practitioner-specific safety boundaries.
Identify the clinical risks in AI-generated interprofessional communications, distinguish simulation AI from clinical AI, and maintain safe patient care during digital system failures ensuring clinical competence is never dependent on system availability.