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 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Interactive oral	AI implementation proposal leadership meeting Interactive discussion on submitted AI implementation strategy proposal.	0%	Multiple weeks	10 minutes	AI prohibited
Outcomes assessed:
In-person practical, skills, or performance task or test	Coding conceptual application challenges 3 challenge questions which get students to apply conceptual understanding of practicals to real biomedical contexts.	10%	Ongoing	10 minutes * 3 per practical	AI prohibited
Outcomes assessed:
Written work	AI implementation in healthcare checkpoints At least 2 of 3 goalposts for AI implementation strategy proposal met by week 7.	20%	Progressive	Variable (see Canvas)	AI allowed
Outcomes assessed:
Written work	AI implementation in healthcare strategy proposal Implementation plan for strategic AI solution based on defined problem(s) within an organisation.	30%	Week 09 Due date: 06 May 2026 at 23:08	3500 words	AI allowed
Outcomes assessed:
Written work	Health Datathon Group Project Plan Progress and completion plan for Datathon project in final month	15%	Week 10 Due date: 10 May 2026 at 23:59	See Canvas	AI allowed
Outcomes assessed:
Presentation	Health Datathon Group Project Prototype Presentation Presentation on the development strategy and function of a simple software prototype	25%	Week 13	10 minutes + 5 minutes Q&A	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Coding Conceptual Application Challenges: Short in-lab challenge questions delivered at key points throughout selected laboratory sessions. Students discuss briefly, then submit individual written responses under secure, non-AI conditions.
AI Implementation in Healthcare Strategy Proposal and Leadership Meeting (Hurdle): A strategic proposal focused on defining organisational problems, ideating AI solution options, selecting and justifying one AI system, and outlining an implementation plan. Includes progress submission that can be submitted flexibly prior to week 7. The proposal is followed by an interactive oral leadership meeting to confirm understanding and allow students to address feedback. Interactive oral acts as a hurdle for the entire assessment (ie at least basic understanding should be shown). Postgrads will be expected to employ more formal data anlayses for problem definition and ideation, and will be expected to provide a stronger evidence-base for their implementation plans.
Health Datathon Group Project (Software Prototype): An open-ended team project where students explore a health dataset and develop a simple software prototype informed by their EDA and/or modelling. Proposal due Week 10, with final prototype presentations due Week 13.

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)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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
Ongoing	AI implementation strategy proposal	Self-directed learning (30 hr)
	Health Datathon Group Project	Self-directed learning (10 hr)
	Ongoing networking + social activities	Seminar (10 hr)
Week 01	Online E-learning: Introduction to Health Data and AI	Self-directed learning (1 hr)
	Unit orientation + intro to AI Implementation assessment	Tutorial (2 hr)
	Data Loading, Access, and Plotting	Practical (2 hr)
Week 02	Online E-Learning: Health Data Context	Self-directed learning (1 hr)
	Stakeholder Communication + Understanding Health Stakeholder Needs	Tutorial (2 hr)
	Combining and Extracting Health Data	Practical (2 hr)
Week 03	AI tools + Cogniti AI agent development	Tutorial (2 hr)
Week 03	Health Datathon Group Project	Practical (2 hr)
Week 04	Online E-Learning: Health Data Quality	Self-directed learning (1 hr)
	Interviewing Stakeholders + Ethics	Tutorial (2 hr)
	Health Data Cleaning	Practical (2 hr)
Week 05	Online E-Learning: Exploratory Data Analysis	Self-directed learning (1 hr)
	Problem definition and ideation	Tutorial (2 hr)
	Exploratory Data Analysis	Practical (2 hr)
Week 06	Smart data	Tutorial (2 hr)
Week 06	Health Datathon Group Project	Practical (2 hr)
Week 07	Online E-Learning: Predictive analytics	Self-directed learning (1 hr)
	Report writing	Tutorial (2 hr)
	Predictive analytics	Practical (2 hr)
Week 08	Online E-Learning: Artificial Neural Networks (ANNs) and model evaluation	Self-directed learning (1 hr)
	AI DEBATE!!!!	Tutorial (2 hr)
	ANNs and Model Evaluation	Practical (2 hr)
Week 09	AI ESCAPE ROOM!!!!	Tutorial (2 hr)
Week 09	Health Datathon Group Project	Practical (2 hr)
Week 10	Health Datathon Group Project	Practical (2 hr)
Week 11	Health Datathon Group Project	Practical (2 hr)
Week 12	Health Datathon Group Project	Practical (2 hr)
Week 13	Health Datathon Group Project	Practical (2 hr)

Attendance and class requirements

Tutorials in weeks 2-7 are mandatory due to assessment components being covered.

Labs in weeks 3, 6, 10-13 are mandatory due to assessment components being covered. While the remaining weeks are flexible, there are marks attached to those classes. These coding conceptual application challenge questions are offered across these laboratory sessions, with more opportunities provided than are required to achieve full credit. Attendance decisions should be guided by individual performance on the challenges.

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. 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.

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.

Quizzes replaced by interspersed in-lab challenge questions which allow for group discussion but submitted individually and securely. Interactive oral added to follow-up AI implementation plan to ensure assessment integrity and to secure ULOs. ULOs updated to be more specific and action-based. E-learning time halved to prioritise active learning in-class over recorded content. Option for simulated stakeholder interviews added in case students cannot connect with real stakeholders. Students need to submit only 2 of 3 checkpoints for AI implementation proposal by week 7. Specific weeks are flexible, as long as they submit 2. Datathon video dropped - now only project plan and final presentation. For added complexity, a simple prototype must be developed.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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)

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, 2026 [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)

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