Analysis of Retrospectively Collected MRI Referral DataAutoProMptNCT07918313
Automated Vetting and Protocolling of MRI Examinations
An observational study of Analysis of Retrospectively Collected MRI Referral Data, run by Edozie Iweka.
Trial summary
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Analysis of Retrospectively Collected MRI Referral Data
- How many people
- 900,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Feb 2027to 28 Sep 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
Read more
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.
- Being tested
- No Intervention: Observational Cohort
- Keywords
- Artificial Intelligence, Large Language Models, Retrieval Augmented Generation, Magnetic Resonance Imaging (MRI), protocolling, Vetting, Explainability.
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No Intervention: Observational CohortOther
No Intervention as this is an experiment on retrospective collected referral data
| Study group | Role | What they receive |
|---|---|---|
| Group | Adult Patients undergoing MRI examinations Receives: Other: No Intervention: Observational Cohort |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
Accuracy of pretrained LLMs+RAG in automating vetting and protocolling of MRI examination referrals.
Measured: Through study completion, an average of 2 years
Secondary outcomes (1)
Clinical Usefulness
Clinical usefulness of explainability reports on the performance of pretrained LLMs+RAG in automating vetting and protocolling of MRI examination referrals.
Measured: Through study completion, an average of 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Nov 2026
- Study start1 Feb 2027 (estimated)
- Primary completion1 Apr 2027 (estimated)
- Study completion28 Sep 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Analysis of Retrospectively Collected MRI Referral DataStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 900,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Edozie Iweka
- Sponsor type
- University, hospital or other
- Collaborators
- University of Bath
- Responsible party
- Sponsor-investigator, Edozie Iweka, University of Bath
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07918313
- Sponsor's protocol ID
- 18376
- Acronym
- AutoProMpt
- Status last verified
- Nov 2026
- First submitted
- 28 Oct 2026
- Last updated
- 5 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 5 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07918313
- Acronym / sponsor ID
- AutoProMpt / 18376
- Keywords
- Artificial Intelligence, Large Language Models, Retrieval Augmented Generation, Magnetic Resonance Imaging (MRI), protocolling, Vetting, Explainability.
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 900,000 participants (estimated)
- Lead sponsor
- Edozie IwekaUniversity, hospital or other
- Collaborators
- University of Bath
- Study start
- 1 Feb 2027 (estimated)
- Primary completion
- 1 Apr 2027 (estimated)
- Study completion
- 28 Sep 2029 (estimated)
- First posted
- 5 Nov 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Group | – | Other: No Intervention: Observational CohortAdult Patients undergoing MRI examinations |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No Intervention: Observational CohortNo Intervention as this is an experiment on retrospective collected referral data | Other | Group |
Primary outcome
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.
1 secondary outcome
Clinical Usefulness
Time frame: Through study completion, an average of 2 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Nov 2026.
- Individual participant dataSharing planned: No. 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.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Analysis of Retrospectively Collected MRI Referral Data, run by Edozie Iweka.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 5 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Edozie Iweka, MSc (Dist) (+441225386989, eoi29@bath.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: Through study completion, an average of 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Nov 2026.