CLIN03 Exploring Artificial Intelligence Tools to Enhance Student Learning in Medical Imaging

Overview

The integration of artificial intelligence (AI) into Medical Imaging (MI) is transforming the field and reshaping radiographic practice. As AI becomes more prevalent in MI practice, there's a growing recognition of the need to equip medical radiation students with the knowledge and skills to utilise AI technologies. Recent updates from the Medical Radiation Practice Board (MRPBA) published in October 2022 underscore the importance of preparing graduates to work alongside AI and emphasise the need for educational programs to adapt accordingly.

This project will investigate the various practice impacts of new AI tools to help enhance the medical imaging curriculum. Our primary aim is to evaluate practitioner insight on the effectiveness of AI tools and assess their potential impact on student learning.

The outcomes of this research will provide valuable insights for aligning our educational curriculum with AI trends to ensure compliance with professional accreditation. Moreover, our findings will provide educators with evidence-based knowledge of AI technologies, enabling them to enhance teaching practices and better prepare students for future roles in the field.

Research engagement

Conduct a literature review and analyse survey data (collected during semester 2, 2024).

Additionally, draft a paper to document these findings and prepare the resulting manuscript for submission to academic journals.

Research activities

The student will work with the entire research team, reviewing and coding data for analysis, performing basic statistical analysis of the data, and preparing results for publication by drafting a manuscript.

Outcomes

Following data analysis and literature review, a draft manuscript for publication in a peer-reviewed journal will be completed by the end of the VRES period.

Skills and experience

A background understanding of medical imaging practice and workflow is highly desirable. Students in second and third-year MI undergraduate programs are encouraged to apply.

Familiar with literature review methods, basic statistical data analysis, and manuscript drafting.

Start date

1 November, 2024

End date

31 January, 2025

Location

On Campus or Remote

Additional information

Statistical software will be available through QUT and access to a QUT hot desk on QUT campus if required

Keywords

• Medical Imaging
• Artificial Intelligence
• Curriculum Design

Contact

Noirin Neligan (n.neligan@qut.edu.au)