ENN662 Machine Learning in Healthcare
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | ENN662 |
|---|---|
| Prerequisite(s): | ENN543 or CAB420. ENN543 can be enrolled in the same teaching period as ENN662. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,192 |
| Domestic tuition unit fee | $5,364 |
| International unit fee | $8,052 |
Unit Outline: Semester 2 2026, Gardens Point, Internal
| Unit code: | ENN662 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | ENN543 or CAB420. ENN543 can be enrolled in the same teaching period as ENN662. |
| Coordinator: | Olivier Salvado | olivier.salvado@qut.edu.au |
Overview
Building on content in prior AI and Machine Learning units, this unit will explore advanced machine learning topics and AI in the context of healthcare. It focuses on real-world clinical and medical applications, data challenges, and relevant models. Topics include performance, deployment and monitoring for screening, diagnosis, prognosis, and treatment monitoring; AI for medical science; natural language processing; computer vision & medical image analysis for radiology. You will learn about how AI technologies is transforming healthcare, how it is used in clinical practice and review the AI specific challenges, principles and research implications. You will also learn about the risk and framework to ensure the responsible use of AI in medical context.
Learning Outcomes
On successful completion of this unit you will be able to:
- Describe and apply advanced concepts in Artificial Intelligence in the context of health problems
- Critically assess Artificial Intelligence applications for healthcare and medical systems and technology applications
- Design, implement, and evaluate an AI system addressing a healthcare problem
- Demonstrate ability to apply research methodologies to apply innovative solutions to challenges in health and medical systems and technology
Content
The content of this unit includes:
1. Review of AI and main machine learning methods
2. Assessing performance for clinical application.
3. Medical Image Analysis
4. Generative AI
5. Health application of computer vision
6. Time-Series Analysis and Sequential Data
7. Biochemistry and drug discovery
8. Genomics and Precision Medicine
9. Explainable AI methods and Responsible AI framework
Learning Approaches
In this unit you can expect to experience the following timetabled activities:
- Formal lectures from academics and experienced professional to give you insight into knowledge, skills, and attributes. You have the opportunity to ask questions during these lectures.
- Tutorial classes that will give you the opportunity to work collaboratively with your peers to solve problems. They will be facilitated by a tutor and will provide an opportunity to test your understanding and gain feedback on your work. These activities will be detailed by week of semester on the unit schedule. You can also expect to be provided with with learning resources including video presentations and readings on a unit Canvas site, which you can access flexibly to complete your learning in this unit. At the beginning of the unit, you will be made aware of the ways in which you can ask questions or seek clarification from the unit coordinator, lecturers and tutor.
You are expected to:
- Engage with timetabled learning activities on campus and ask questions.
- Engage with online resources outside of timetabled learning activities. They will be available on the unit Canvas site. You will receive regular email announcements regarding the release of these resources.
- Work individually and in a group to complete assessments. While there will be time during timetabled tutorial classes, you will also need to undertake independent work outside of this time to complete assessment tasks, including for areas of individual responsibility.
- Prepare for learning activities according to the unit schedule and follow up on any work not completed.
- Complete assessment tasks by working consistently throughout the semester and meeting the due dates that are published via the unit Canvas site.
Feedback on Learning and Assessment
During tutorial classes, with your group you will share your formative ideas for your project/assessment and you will receive feedback from your tutor. You are encouraged to view your group as a learning community and to share and discuss emergent ideas and your understandings. Each assessment submission will be graded against criteria and standards that will be shared with you at the beginning of semester through Assessment Task Descriptions and Marking Rubrics. Marked assessment will include feedback given by the markers against the criteria.
Assessment
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Existing Conditions Analysis
Students will undertake and conditions analysis to identify critical features of an existing health related problem and critically discuss the current state of the system.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project Conditions Analysis
Students will undertake a conditions analysis on a hypothetical project to identify potential risks and challenges, maximise benefits and discuss outcomes.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (practical)
Students will undertake an analysis task in real time to determine requirements, risk, opportunities and benefits, and discuss their implementation.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
Resource Materials
Other
Learning materials in this unit and links to online resources will be managed from its Canvas site.
Risk Assessment Statement
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN56 Master of Engineering Technology
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices as they relate to contemporary practice in Engineering Technology
Relates to: Existing Conditions Analysis , Examination (practical) - Analyse and evaluate Engineering Technology problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Existing Conditions Analysis , Project Conditions Analysis - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Engineering Technology in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Existing Conditions Analysis , Project Conditions Analysis , Examination (practical) - Demonstrate an ability to research and apply established theories and plan and execute a substantial research-based project, cultivating an attitude of engineering innovation
Relates to: Project Conditions Analysis
EN57 Master of Biomedical Systems and Technology
- Demonstrate and apply advanced specialist discipline knowledge, concepts and practices in the context of contemporary Biomedical Engineering practice, Technologies and Systems thinking.
Relates to: Existing Conditions Analysis , Examination (practical) - Employ advanced technical knowledge, informed by contemporary practice, and inclusive of user and system needs, to the design and critical analysis of innovative solutions to Biomedical challenges in Healthcare.
Relates to: Existing Conditions Analysis , Project Conditions Analysis - Apply innovative, systematic frameworks to plan, design, manage and deliver projects where knowledge of Biomedical Systems and Technology are critical to enacting change in Healthcare.
Relates to: Existing Conditions Analysis , Project Conditions Analysis , Examination (practical) - Demonstrate an ability to research and apply established theories, and plan and execute a substantial research-based project, cultivating an attitude of engineering innovation.
Relates to: Project Conditions Analysis
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: Existing Conditions Analysis , Examination (practical) - Analyse and evaluate Engineering problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Existing Conditions Analysis , Project Conditions Analysis - Apply innovative, systematic approaches to plan, design, deliver and manage Engineering projects in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Existing Conditions Analysis , Project Conditions Analysis , Examination (practical)