Automated Vetting and Protocolling of MRI Examinations (AutoProMpt)

November 2, 2026 updated by: Edozie Iweka

Automated Vetting and Protocoling of MRI Examination Referrals Using Explainable Large Language Models (LLM) With Retrieval Augmented Generation (RAG) (XLLM) Techniques- A Multi-stage Study

Background Vetting and assigning of protocols to MRI scan requests are usually carried out by radiologists and radiographers. This way is both time-consuming and prone to error. Recent improvements in Artificial Intelligence (Al) like Large Language Models (LLMs) are better at processing human language. It could be used to improve tasks like image reporting and assigning protocols in radiology.

Approach Firstly, I will find out about the delays in the way this is currently done. I will use a survey to find out the perceptions of radiologists to both the current process and the proposed Al intervention.

I will then use an LLM to undertake referral and protocol tasks. I will compare its performance to the performance of clinical specialists and established guidelines.

I will present the explanation reports to radiologists and ask them if they can be understood and whether they can be trusted as clinically reliable.

Impact My research could help reduce the workload and time radiologists spend dealing with scan requests. The findings will help design future research to consider application in real clinical settings.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

900000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Edozie Iweka, MSc (Dist)
  • Phone Number: +441225386989
  • Email: eoi29@bath.ac.uk

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

This study is multi-stage multi-center research involving retrospective MRI referral data of patients and collected across three NHS Foundation Trusts in the United Kingdom.

Description

Inclusion Criteria:

  • Patients who have had MRI examinations in the study sites
  • Patients with MRI referrals submitted through electronic referral systems
  • Patients whose MRI referrals were vetted and protocolled prior to examination

Exclusion Criteria:

  • Patients who have had other imaging examination outside the MRI
  • Patients with paper referrals scanned in electronic referral systems
  • Patients for MRI examinations without vetting and protocolling

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: COHORT
  • Time Perspectives: RETROSPECTIVE

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Group

Adult Patients undergoing MRI examinations
Other: No Intervention: Observational Cohort

No Intervention as this is an experiment on retrospective collected referral data

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy
Time Frame: Through study completion, an average of 2 years

Accuracy of pretrained LLMs+RAG in automating vetting and protocolling of MRI examination referrals.
Through study completion, an average of 2 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Clinical Usefulness
Time Frame: Through study completion, an average of 2 years

Clinical usefulness of explainability reports on the performance of pretrained LLMs+RAG in automating vetting and protocolling of MRI examination referrals.
Through study completion, an average of 2 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Edozie Iweka

Collaborators

University of Bath

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

February 1, 2027

Primary Completion (Estimated)

April 1, 2027

Study Completion (Estimated)

September 28, 2029

Study Registration Dates

First Submitted

October 28, 2026

First Submitted That Met QC Criteria

November 2, 2026

First Posted (Actual)

November 5, 2026

Study Record Updates

Last Update Posted (Actual)

November 5, 2026

Last Update Submitted That Met QC Criteria

November 2, 2026

Last Verified

November 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 18376

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

No patient identifiable data will be collected from this study. All collected data will be held by the sponsor institution, University of Bath as per local research data policy.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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