AI in Healthcare: Unit Overview¶
Unit Description¶
This unit examines the role of Artificial Intelligence (AI) in contemporary multidisciplinary healthcare, with a focus on AI architecture, safe implementation, ethical considerations, and emerging innovations. Students will critically evaluate AI technologies in healthcare settings, assess their advantages and risks, and explore future trends in AI-driven multidisciplinary healthcare.
Context¶
The unit is designed for health practitioners in the Australian context, with particular emphasis on aeromedical retrieval. This focus provides a lens through which to examine AI applications in high-stakes, time-critical, and resource-constrained environments.
Learning Outcomes¶
Upon successful completion of this unit, students will be able to:
- Explain the foundations of AI Architecture – Analyse the core components, algorithms, and models used in healthcare AI applications.
- Critically evaluate AI Safety – Assess the challenges, risks, and ethical implications of AI in clinical settings.
- Implement and monitor AI in Healthcare – Apply best practices for integrating AI solutions safely and effectively.
- Analyse Current and Emerging AI Technologies – Investigate existing AI applications and predict future advancements in healthcare.
Pedagogical Framework¶
Learning Arc¶
The unit follows a scaffolded progression:
| Phase | Weeks | Focus | Cognitive Level |
|---|---|---|---|
| Foundations | 1-3 | Core concepts, architecture, terminology | Knowledge & Comprehension |
| Evaluation | 4-6 | Critical assessment, risk analysis, ethics | Analysis & Evaluation |
| Application | 7-9 | Implementation, integration, monitoring | Application & Synthesis |
| Synthesis | 10-12 | Future directions, position-taking | Synthesis & Creation |
Practitioner Question Progression¶
Each phase addresses a core question that practitioners face when encountering AI:
- Weeks 1-3: "What is this?" – Technical literacy
- Weeks 4-6: "Should we use it?" – Critical evaluation
- Weeks 7-9: "How do we govern it?" – Implementation & oversight
- Weeks 10-12: "What comes next?" – Future readiness
Why Evaluation Before Application?¶
In safety-critical domains like healthcare and aeromedical retrieval, practitioners must understand risks, limitations, and ethical implications before implementing AI systems. This sequencing reflects the precautionary approach appropriate to clinical contexts.
Aeromedical Context Integration¶
Each week includes content specific to retrieval medicine:
| Week | Aeromedical Focus |
|---|---|
| 1 | AI applications in retrieval medicine overview |
| 2 | Architecture choices for different retrieval challenges |
| 3 | Data challenges in pre-hospital and transport contexts |
| 4 | Heightened failure consequences in retrieval |
| 5 | Cross-jurisdictional regulatory complexity |
| 6 | Deep dive: AI in critical and remote care |
| 7 | Multi-site and cross-boundary implementation |
| 8 | Team dynamics and CRM implications |
| 9 | Operational continuity and contingency |
| 10 | International aeromedical AI implementations |
| 11 | Future of retrieval medicine with AI |
| 12 | Strategic positioning for Australian services |
Practical Computing Stream¶
The unit includes hands-on exercises using Google Colab to demystify AI/ML processes. No prior programming experience is required. See Practical Exercises Overview for details.
| Week | Practical Exercise |
|---|---|
| Pre-course | Colab Orientation |
| 1 | Exploring AI Outputs |
| 2 | ML Fundamentals Hands-on |
| 3 | Data Exploration |
| 5 | Bias Detection |
| 7 | Model Validation |
| 10 | LLM Experimentation |
Assessment Overview¶
| Assessment | Weighting | Due | Learning Outcomes |
|---|---|---|---|
| Technical Foundations Analysis | 25% | End Week 4 | LO1 |
| Critical Evaluation Report | 35% | End Week 8 | LO1, LO2 |
| Capstone Project | 40% | Week 12 | LO1, LO2, LO3, LO4 |
See the assessments for detailed briefs and rubrics.
Weekly Schedule Summary¶
Section 1: Foundations (Weeks 1-3)¶
- Week 1: Introduction to AI in Healthcare
- Week 2: AI Architecture and Algorithms
- Week 3: Data Foundations for Healthcare AI
Section 2: Evaluation (Weeks 4-6)¶
- Week 4: AI Safety and Failure Modes
- Week 5: Ethics and Regulatory Landscape
- Week 6: AI in High-Stakes Environments
Section 3: Application (Weeks 7-9)¶
- Week 7: Implementation Frameworks
- Week 8: Human-AI Teaming and Workflow Integration
- Week 9: Monitoring, Maintenance, and Incident Response
Section 4: Synthesis (Weeks 10-12)¶
- Week 10: Current AI Landscape in Healthcare
- Week 11: Emerging Technologies and Trends
- Week 12: Integration and Future Positioning