Outline

Overview

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

Academic unit	Biomedical Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	BMET2925
Assumed knowledge ?	Familiarity with general mathematical and statistical concepts. Online learning modules will be provided to support obtaining this knowledge
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Hamish Fernando, hamish.fernando@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Skills-based evaluation	Health Datathon Group Project A group project developing a solution for an open-ended health data task. Due between weeks 13-Final exam week 1 based on group decision.	20%	Multiple weeks	10-15 minute final information video
Outcomes assessed:
Online task	Weekly Exercises Set of quizzes to be completed between weeks 2-7.	20%	Multiple weeks	Variable
Outcomes assessed:
Small continuous assessment	AI implementation in healthcare research report How could AI address the problems of a specific healthcare organisation? Submission between weeks 9-12 based on student requirements.	20%	Multiple weeks	3500 words
Outcomes assessed:
Small continuous assessment	AI implementation in healthcare checkpoints Progressive series of checkpoints to ensure progress with AI implementation assessment is satisfied.	20%	Progressive	Variable (see Canvas)
Outcomes assessed:
Short release assignment	Health Datathon Group Project Project plan	10%	Week 09 Due date: 02 May 2025 at 23:59	_
Outcomes assessed:
Short release assignment	Health Datathon Group Project Individual discussion of project approach	10%	Week 12 Due date: 23 May 2025 at 23:59	5 minutes
Outcomes assessed:
= group assignment ? = AI allowed ?

Assessment summary

Weekly Exercises: A set of weekly exercises delivered via Canvas. This will include short quizzes and submissions based on work undertaken during the tutorials and laboratories, as well as weekly checkpoint descriptions of the major assignments.
AI implementation in healthcare research report: A report based on research into the implementation of AI into a specific healthcare organisation. Strategic checkpoints at weeks 2, 6 and 8 done in pairs ensure students are kept on-track. in pairs. Final submission can be done individually or is pairs (individual by default, pairs only of both agree). Due date has been made flexible, and must be agreed upon by the group in the week 8 checkpoint submission. That agreed date will be fixed, no simple extension allowed. If any delay, special con is required.
Health Datathon Group Project: An open-ended project where students will work in teams to create a solution for a health challenge. Strategic checkpoints at weeks 9 and 13 ensure students are kept on track.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (Schedule 1).

Result code	Result name	Mark range	Description
HD	High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
DI	Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
CR	Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
PS	Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
FA	Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.
AF	Absent fail	0 - 49	When you haven’t completed all assessment tasks or met the attendance requirements.
CN	Cancelled	No mark	When your enrolment has been cancelled.
DC	Discontinued not to count as failure	No mark or 0	When you discontinue a unit under special circumstances (outlined in clause 92 of the Coursework Policy), after the relevant census date.
DF	Discontinue – fail	No mark or 0	When you discontinue a unit after the relevant census date but before the DF deadline, and you have not been granted a discontinuation under special circumstances.
FR	Failed requirements	No mark	When you don’t meet the learning outcomes to a satisfactory standard, for units which are marked as either Satisfied requirements or Failed requirements.
SR	Satisfied requirements	No mark	When you meet the learning outcomes to a satisfactory standard, for units which are marked as either Satisfied requirements or Failed requirements.
WD	Withdrawn	No mark	When you discontinue a unit before the relevant census date. WD grades do not appear on your academic transcript.
NE	Not examinable	No mark or 0	When you have exhausted your options to sit replacement exams or replacement assessment tests. An NE does not count as a fail on your transcript and won’t be included in your weighted average mark (WAM).

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Formal exam period	Health Datathon Group Project	Independent study (2 hr)
Formal exam period	Health Datathon Group Project	Project (2 hr)
Week 01	Online E-learning: Introduction to Health Data and AI	Independent study (2 hr)
	Introduction to unit + AI	Tutorial (2 hr)
	Data Loading, Access, and Plotting	Computer laboratory (2 hr)
	AI implementation in healthcare research report	Independent study (2 hr)
Week 02	Online E-Learning: Health Data Context	Independent study (2 hr)
	Stakeholder Communication + Understanding Health Stakeholder Needs	Tutorial (2 hr)
	Combining and Extracting Health Data	Computer laboratory (2 hr)
	AI implementation in healthcare research report	Independent study (3 hr)
Week 03	Online E-Learning: Health Data Quality	Independent study (2 hr)
	The Impact of Health Data Quality	Tutorial (2 hr)
	Health Data Cleaning	Computer laboratory (2 hr)
	AI implementation in healthcare research report	Independent study (3 hr)
Week 04	Online E-Learning: Exploratory Data Analysis	Independent study (2 hr)
	Optimising Data Use + Review of Descriptive Statistics	Tutorial (2 hr)
	Exploratory Data Analysis	Computer laboratory (2 hr)
	AI implementation in healthcare research report	Independent study (3 hr)
Week 05	Online E-Learning: Predictive Analysis	Independent study (2 hr)
	Interviewing Stakeholders + Ethics	Tutorial (2 hr)
	Implementing Predictive Models	Computer laboratory (2 hr)
	AI implementation in healthcare research report	Independent study (3 hr)
Week 06	Online E-Learning: Evaluating AI and Data Solutions	Independent study (2 hr)
	Cross Validation	Computer laboratory (2 hr)
	AI implementation in healthcare research report	Independent study (3 hr)
Week 07	Online E-Learning: Image/signal/text analysis	Independent study (2 hr)
	Ideation	Tutorial (2 hr)
	Image/signal/text analysis	Computer laboratory (2 hr)
	AI implementation in healthcare research report	Independent study (3 hr)
Week 08	Guest lecture: Implementation and Impact in Health	Lecture (2 hr)
	Review + Health Datathon Group Project	Computer laboratory (2 hr)
	AI implementation in healthcare research report	Independent study (3 hr)
Week 09	Guest lecture: AI Systems in Data and Health - Today	Independent study (2 hr)
	Health Datathon Group Project	Project (2 hr)
	AI implementation in healthcare research report	Independent study (3 hr)
Week 10	Online E-Learning: AI Systems in Data and Health - Future	Independent study (2 hr)
	Health Datathon Group Project	Independent study (2 hr)
	Health Datathon Group Project	Project (2 hr)
Week 11	Health Datathon Group Project	Independent study (2 hr)
Week 11	Health Datathon Group Project	Project (2 hr)
Week 12	Health Datathon Group Project	Independent study (2 hr)
Week 12	Health Datathon Group Project	Project (2 hr)
Week 13	Health Datathon Group Project	Independent study (2 hr)
Week 13	Health Datathon Group Project	Project (2 hr)
Week 14 (STUVAC)	Health Datathon Group Project	Independent study (2 hr)
Week 14 (STUVAC)	Health Datathon Group Project	Project (2 hr)

Attendance and class requirements

Tutorials will take place in weeks 1-9. Tutorials in weeks 2, 3 have restricted open book (no AI) quizzes attached to them. Tutorials in weeks 4, 5 and 7 will have discussions central to assessments and are therefore mandatory to attend. Week 6, 8 and 9 tutorials optional, and will be focused on providing additional help for assessments.

There will be labs taking place every week. Labs in weeks 4-7 have restricted open book (no AI) quizzes attached to them at the end of the lab. Late entry to lab will not be allowed unless permission has been obtained.

Post-midsem labs are mandatory for all students in order to work on the Datathon together in-class in their groups, with the exception of week 13. If a student cannot attend labs between weeks 9 and 12, special con is required.

Attendance to the two guest lectures are mandatory.

The University attendance policy can be found here (see clause 68): https://www.sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2014/378&RendNum=0

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

References and readings will be provided on Canvas.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Discuss the importance of data and AI for modern society in health, including how these may be implemented in specific healthcare contexts, 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. Develop administrative and communication skills such as contacting, planning and conducting interviews/surveys with a variety of healthcare stakeholders in order to understand how AI models could benefit their contexts.
LO4. 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.
LO5. Apply machine learning techniques such as support vector machines and neural networks to solve problems on health datasets.
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.
LO8. Communicate the results of a data analytics pipeline in an oral and written form to an audience that may comprise non-experts.
LO9. Understand how recent publicly available AI models, particularly generative AI, could be used to enhance the process of engineering design thinking, while realising its limitations.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Class schedule has been optimised to allow for better assessment support. Quizzes added to end of some labs in order to ensure technical understanding. Lab activities made more concise in order to introduce elements of Datathon project into workflow. This allows students to start working lightly on their Datathon project early in the semester. Guest lectures added for AI implementation and AI in Healthcare: Today and in Future. AI Implementation in Healthcare final report submission has been made flexible (anywhere between weeks 9 and 12). Pair to decide on an ideal date for both, and confirm within week 8 checkpoint submission. This date will be fixed, and no simple extension will be allowed.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

BMET9925: AI, Data, and Society in Health

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links