Artificial Intelligence for Detecting Additional Findings on cross sectional imaging datasets: Clinical Relevance and Patient Outcomes

Summary

Artificial intelligence (AI) is increasingly being introduced into medical imaging to support the interpretation of radiographs. Cross sectional imaging ( e.g. Computed Tomography, Magnetic Resonance Imaging scans) is frequently performed, yet abnormalities may remain undetected when image interpretation is focused primarily on answering a specific clinical question. AI may provide an additional opportunity to identify abnormalities that were not recognised during the original interpretation, but the clinical relevance and consequences of such findings require further investigation.

This PhD will investigate the ability of an AI system to identify additional findings on cross sectional imaging datasets and evaluate their potential clinical importance. The research will be conducted in three phases as follows:

* Phase 1: The first phase will examine anonymised clinical cross-sectional images alongside original imaging reports and relevant clinical information. An AI system will be applied to the images to identify discrepancies between the original human interpretation and AI-supported findings.

* Phase 2: The second phase will investigate the subsequent patient journey for cases in which AI identifies potentially relevant additional findings. Available clinical information will be examined over time to determine whether further investigations, diagnoses or clinical outcomes support the relevance of these findings.

* Phase 3: The final phase will explore the views of patients, service users and healthcare professionals regarding the identification and management of additional findings using AI. This phase will examine the acceptability, perceived benefits, concerns and potential challenges associated with this approach.

The findings will contribute to understanding whether AI can provide clinically useful additional assurance during image interpretation and how such technology might be appropriately integrated into clinical practice. The project will have relevance for medical imaging, radiography, radiology and the safe development of AI-supported patient care.

AccessNI clearance required

Please note, the successful candidate will be required to obtain AccessNI clearance prior to registration due to the nature of the project.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• International applicants should hold, or expect to hold a relevant Masters Degree at a level equivalent to a distinction at Ulster
• Practice-based research experience and/or dissemination
• Experience or knowledge of research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Track record of peer reviewed publications
• Experience of presentation of research findings
• Relevant professional qualification

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

PhD scholarships are available for this project to all applicants worldwide, regardless of residency or domicile, and will cover:

• Tuition fees.
• A maintenance allowance of approximately £22,000 (exact rate is to be confirmed) per annum for three years, subject to satisfactory academic performance.
• A Research Training Support Grant (RTSG) of approximately £900 per annum.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year, or part-time equivalent, funded from any source, including self-financing students, are NOT eligible to apply for an award.

Students in full-time employment are not eligible for an award.

Part-time PhD scholarships may be available at a 0.5 pro rata rate of the full-time scholarship, which would require a six-year registration period.

Due consideration should be given to financing your studies.

The University will award a successful candidate one of the following scholarships for the project, depending on the funding available at the time an admissions offer is made:

1. Department for the Economy (DfE) PhD Scholarships
2. Vice Chancellor’s Research Scholarships (VCRS)

Please note that the Terms and Conditions for scholarships are subject to change for 2027/28.