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Unit of study_

BMET9925: AI, Data, and Society in Health

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.

Code BMET9925
Academic unit Biomedical Engineering
Credit points 6
Prerequisites:
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None
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. Discuss the importance of data and AI for modern society in health, using appropriate literature to explain their reasoning.
  • LO2. Articulate the challenges in working with real-world health datasets and select an appropriate data analytics or AI solution for a given health problem, with sufficient justification for the choice.
  • LO3. Characterise the impact of AI and data analytics solutions on different health stakeholder groups, in terms of technical, legal, ethical, economic, and social benefits and limitations.
  • LO4. Apply machine learning techniques such as support vector machines and neural networks to solve problems on health datasets.
  • LO5. Communicate the results of a data analytics pipeline in an oral and written form to an audience that may comprise non-experts.
  • LO6. Understand and apply fundamental data analytics processes such as problem definition, data collection, data cleaning, exploratory data analysis, modelling, and visualisation.
  • LO7. Use code libraries and toolboxes for simple data analysis and machine learning tasks in health.

Unit outlines

Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.