Outline

Overview

This unit explores the state of the art and prospects for artificial intelligence applications in medical imaging, including considerations of technique optimisation, data analysis, validation, ethics, and regulation.

Unit details and rules

Academic unit	Clinical Imaging
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Background in medical imaging or medicine
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Ziba Gandomkar, ziba.gandomkar@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
Written work	Critically review the selected papers Identify the potential gaps for the translation of the solution in practice	40%	STUVAC	1000 words	AI allowed
Outcomes assessed:
Written work	Article proposal Proposing a topic related to one of the areas, where AI impacts Med Imaging	10%	Week 05	300-500 words	AI allowed
Outcomes assessed:
Evaluation	Peer review review 2 other students' initial proposal	10%	Week 07	500 words	AI allowed
Outcomes assessed:
Written work	Methodological review Conduct the methodological review of the selected papers	40%	Week 11	500-1000 words	AI allowed
Outcomes assessed:

Assessment summary

Students critically engage with a small selection of AI-in-medical-imaging papers (2-3 papers) through scaffolded assessments that together build towards a coherent critical review. Early tasks focus on topic selection, AI-assisted introductory writing, and exploring AI-based peer feedback. Later tasks focus on a presentation that critically appraises study methods and results, and a written discussion of clinical, translational, and ethical/regulatory implications, informed in part by AI-generated stakeholder perspectives. AI use is structured and documented; assessment emphasises students’ critical thinking and understanding rather than AI output.

Assessment criteria

Result name

Mark range

Description

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.

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.

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.

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.

Fail

0 - 49

When you do not meet the learning outcomes of the unit to a satisfactory standard.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

(a) For work submitted after the deadline but up to three calendar days late, a penalty of 15 per cent of the maximum mark awardable for the assignment will apply. (b) For work submitted after 3 days and less than one week after the deadline, a penalty of 30 per cent of the maximum mark awardable for the assignment will apply. (c) For work submitted more than one week late but less than two weeks after the deadline, a penalty of 50 per cent of the maximum mark awardable for the assignment will apply. (d) Work submitted more than two weeks after deadline will not be assessed (zero mark).

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
Week 01	Brief overview AI in medical imaging	Self-directed learning (2 hr)
Week 02	Basic principles of machine learning	Self-directed learning (2 hr)
Week 03	Basic principles of machine learning models for image analysis	Self-directed learning (2 hr)
Week 04	Basic principles of deep learning	Self-directed learning (2 hr)
Week 05	Underpinning technologies and architectures for deep learning	Self-directed learning (2 hr)
Week 06	Data required for AI development and AI Validation	Self-directed learning (2 hr)
Week 07	AI applications in medical imaging: Image interpretation	Self-directed learning (2 hr)
Week 08	AI applications in medical imaging: Image acquisition	Self-directed learning (2 hr)
Week 09	AI applications in medical imaging: Image segmentation	Self-directed learning (2 hr)
Week 10	AI applications in medical imaging: Image registration	Self-directed learning (2 hr)
Week 11	AI applications in medical imaging: Radiotherapy planning	Self-directed learning (2 hr)
Week 12	AI for Health Services management	Self-directed learning (2 hr)
Week 13	Unsupervised learning and AI for knowledge extraction	Self-directed learning (2 hr)

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.

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 current approaches and methodologies underpinning AI
LO2. Evaluate the potential roles and impact of AI in medical imaging
LO3. Critically evaluate previous validations, ethical and regulatory considerations of clinical AI.

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.

No major changes have been made to the assessment or the outline. In response to students’ feedback on previous unit offerings, I only changed the deadlines for submitting the assessments and made them AI-aware.

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

MRTY5210: Artificial Intelligence in Medical Imaging

Semester 1, 2026 [Online] - 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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links