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Unit of study_
BMET9925: AI, Data, and Society in Health
2026 unit information
Unprecedented growth in computing power, the advent of artificial intelligence (AI)/machine learning technologies, and global data platforms are changing the way in which we approach real-world healthcare challenges. This interdisciplinary unit will introduce students from different backgrounds to the fundamental concepts of data analytics and AI, and their practical applications in healthcare. Throughout the unit, students will learn about the key concepts in data analytics and AI techniques, and obtain hands-on experience in applying these techniques to a broad range of healthcare problems. At the same time, they will develop an understanding of the ethical considerations in health data analytics and AI, and how their use impacts society: from the patient, to the doctor, to the broader community. A key element of the learning process will be a team-based Datathon project where students will deploy their knowledge to address an open-ended healthcare problem, in particular developing a practical solution and analysing how it's use may change things in the healthcare domain. Upon completion of this unit, students will understand and be able to enlist data analytics and AI tools to design solutions to healthcare problems.
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Postgraduate |
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| Academic unit | Biomedical Engineering |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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None |
| Prohibitions:
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BMET2925 |
| Assumed knowledge:
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Familiarity with general mathematical and statistical concepts. Online learning modules will be provided to support obtaining this knowledge |
At the completion of this unit, you should be able to:
- LO1. Analyse and justify the role of data analytics and AI in addressing real-world healthcare challenges, using appropriate academic and professional sources.
- LO2. Formulate and justify well-scoped healthcare problems suitable for AI-enabled solutions by analysing organisational context, constraints, and objectives.
- LO3. Demonstrate informed contextual inquiry by seeking, evaluating, and synthesising stakeholder perspectives and/or alternative evidence sources to inform AI solution design.
- LO4. Implement a basic data analytics and machine learning workflow for health data using appropriate coding tools, including data preparation, modelling, and visualisation.
- LO5. Evaluate the feasibility, risks, and broader implications of AI and data analytics solutions in healthcare, including security, technical, ethical, legal, economic, and social considerations.
- LO6. Communicate data-driven insights and AI-informed design decisions clearly in written and oral formats for non-expert audiences, including a critical awareness of the capabilities and limitations of generative AI tools.
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