Artificial Intelligence Perception and AttitudeNCT07885150
What Should we Measure? A Qualitative Interview Study on Outcomes for Evaluating AI-assisted Medical Image Interpretation.
An observational study of Artificial Intelligence Perception and Attitude, run by University of Oxford at 1 site in United Kingdom.
Trial summary
Background: AI (artificial intelligence) tools are increasingly used to help clinicians interpret medical images such as X-rays, scans, skin photographs and eye images. In most cases the AI does not work alone: a clinician makes the final decision, using the AI as support. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence Perception and Attitude
- How many people
- 30 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 16 Oct 2026to 16 Oct 2027
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The investigators expect to involve approximately 24-30 participants in total, each taking part in a single interview. Participants will be purposively sampled across the following stakeholder groups, with roughly equal numbers. A key priority is to achieve a rich spread of perspectives within and across the groups.
Who may be able to join
- Membership of one of the following stakeholder groups:
- Clinicians who interpret medical images (e.g. radiologists, dermatologists, ophthalmologists, pathologists, and trainees in these specialties)
- Researchers and methodologists who design or analyse studies of these tools
- Developers of AI image-interpretation tools
- Regulatory, health-technology-assessment and policy stakeholders
- Patients and members of the public, including people affected by conditions diagnosed using medical imaging
- Hospital managers and procurers
Who cannot join
- Involvement in study design
- Unable to speak fluent English.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom1 site
Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences
Oxford, OX37LD
About this study
Official title: What Should we Measure? A Qualitative Interview Study on Outcomes for Evaluating AI-assisted Medical Image Interpretation
Background:
AI (artificial intelligence) tools are increasingly used to help clinicians interpret medical images such as X-rays, scans, skin photographs and eye images. In most cases the AI does not work alone: a clinician makes the final decision, using the AI as support. Before these tools are used in the NHS, they must be tested to show they are safe and helpful. They are usually tested in a "reader study", where several clinicians each interpret the same set of images, sometimes with the AI's help and sometimes without, so performance can be compared. These studies tend to focus on whether the diagnosis was correct. However, accuracy alone may not measure everything that matters. For example, what happens when the AI gets it wrong, whether clinicians rely on it too much or too little, or how the tool affects efficiency. At present there is no agreed standard for what these studies should measure, so important things are often missed and studies are hard to compare.
Read more
Aim:
The aim of this study is to find out, by talking to people with different perspectives, which outcomes are important when evaluating AI tools that help clinicians read medical images.
Objectives:
- Explore which outcomes people think matter when judging whether an AI tools is safe and good enough to use
- Understand the reasons behind their views
- Explore how priorities differ between groups, such as patients and developers.
The output will be a list of important candidate outcomes, which will feed into a future project to agree a recommended set of outcomes for these studies (a "core outcome set").
Research value:
Without agreement on what to measure, studies of AI tools may overlook outcomes that matter, and their results are difficult to compare and combine. Hearing directly from the people affected, rather than relying only on what previous studies happened to measure, helps ensure the recommended set reflects people's priorities. This will lead to better-designed studies and AI tools that are safer and more useful in practice.
Outline of the research:
The investigators will interview 24-30 people who have a stake in how these tools are tested and used, including clinicians, researchers, developers, people involved in regulation, and patients and members of the public. Interviews will last 45-60 minutes and will take place using Microsoft Teams. The investigators will ask people what they think is important to measure and why, and then explore which of those things could realistically be measured in a reader study.
Risks/benefits:
This is a low-risk study, with no direct benefits to participants. The topics concern methodological matters rather than people's personal health, so personal distress is not anticipated. Taking part is voluntary: interviewees can skip any question or stop at any time. The wider benefit will be better testing of AI imaging tools in future.
Impact:
The interviews will inform the candidate list of outcomes for the future project and the investigators will publish and present the findings.
- Keywords
- artificial intelligence, perspectives, core outcome set
- Medical subject headings
- Behavior
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Patients and the public | Not described | |
| Clinicians | Not described | |
| Representatives of UK regulatory bodies | Not described | |
| Researchers and methodologists | Not described | |
| Hospital managers and procurers | Not described | |
| Developers of AI image-interpretation tools | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Qualitative themes and subthemes
Measured: Day 1 (interview date) through study completion (1 year)
Secondary outcomes (1)
Number of participants per stakeholder group
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Oct 2026
- Study start16 Oct 2026 (estimated)
- Primary completion16 Jul 2027 (estimated)
- Study completion16 Oct 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial Intelligence Perception and AttitudeStudies of the same condition, open and under way.
- More trials run by University of OxfordThe sponsor's other registered studies.
- 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
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 30 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Oxford
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Dominic Furniss, DM MA MBBCh FRCS, Study chair, University of Oxford
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07885150
- Sponsor's protocol ID
- MSDREC 3784137
- Other IDs
- DRF302562
- Status last verified
- Oct 2026
- First submitted
- 8 Oct 2026
- Last updated
- 21 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Qualitative interview data may include sensitive personal health, commercial, or regulatory information.
Study at a glance
From the registry record, last updated by the sponsor on 21 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- What Should we Measure? A Qualitative Interview Study on Outcomes for Evaluating AI-assisted Medical Image Interpretation
- ClinicalTrials.gov ID
- NCT07885150
- Acronym / sponsor ID
- MSDREC 3784137
- Keywords
- artificial intelligence, perspectives, core outcome set
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 30 participants (estimated)
- Lead sponsor
- University of OxfordUniversity, hospital or other
- Study start
- 16 Oct 2026 (estimated)
- Primary completion
- 16 Jul 2027 (estimated)
- Study completion
- 16 Oct 2027 (estimated)
- First posted
- 21 Oct 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:
AI (artificial intelligence) tools are increasingly used to help clinicians interpret medical images such as X-rays, scans, skin photographs and eye images. In most cases the AI does not work alone: a clinician makes the final decision, using the AI as support. Before these tools are used in the NHS, they must be tested to show they are safe and helpful. They are usually tested in a "reader study", where several clinicians each interpret the same set of images, sometimes with the AI's help and sometimes without, so performance can be compared. These studies tend to focus on whether the diagnosis was correct. However, accuracy alone may not measure everything that matters. For example, what happens when the AI gets it wrong, whether clinicians rely on it too much or too little, or how the tool affects efficiency. At present there is no agreed standard for what these studies should measure, so important things are often missed and studies are hard to compare.
Aim:
The aim of this study is to find out, by talking to people with different perspectives, which outcomes are important when evaluating AI tools that help clinicians read medical images.
Objectives:
- Explore which outcomes people think matter when judging whether an AI tools is safe and good enough to use
- Understand the reasons behind their views
- Explore how priorities differ between groups, such as patients and developers.
The output will be a list of important candidate outcomes, which will feed into a future project to agree a recommended set of outcomes for these studies (a "core outcome set").
Research value:
Without agreement on what to measure, studies of AI tools may overlook outcomes that matter, and their results are difficult to compare and combine. Hearing directly from the people affected, rather than relying only on what previous studies happened to measure, helps ensure the recommended set reflects people's priorities. This will lead to better-designed studies and AI tools that are safer and more useful in practice.
Outline of the research:
The investigators will interview 24-30 people who have a stake in how these tools are tested and used, including clinicians, researchers, developers, people involved in regulation, and patients and members of the public. Interviews will last 45-60 minutes and will take place using Microsoft Teams. The investigators will ask people what they think is important to measure and why, and then explore which of those things could realistically be measured in a reader study.
Risks/benefits:
This is a low-risk study, with no direct benefits to participants. The topics concern methodological matters rather than people's personal health, so personal distress is not anticipated. Taking part is voluntary: interviewees can skip any question or stop at any time. The wider benefit will be better testing of AI imaging tools in future.
Impact:
The interviews will inform the candidate list of outcomes for the future project and the investigators will publish and present the findings.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 6
| Arm | Type | Receives |
|---|---|---|
| Patients and the public | – | – |
| Clinicians | – | – |
| Representatives of UK regulatory bodies | – | – |
| Researchers and methodologists | – | – |
| Hospital managers and procurers | – | – |
| Developers of AI image-interpretation tools | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The investigators expect to involve approximately 24-30 participants in total, each taking part in a single interview. Participants will be purposively sampled across the following stakeholder groups, with roughly equal numbers. A key priority is to achieve a rich spread of perspectives within and across the groups.
Key inclusion criteria (7 listed)
- Clinicians who interpret medical images (e.g. radiologists, dermatologists, ophthalmologists, pathologists, and trainees in these specialties)
- Researchers and methodologists who design or analyse studies of these tools
- Developers of AI image-interpretation tools
- Regulatory, health-technology-assessment and policy stakeholders
- Patients and members of the public, including people affected by conditions diagnosed using medical imaging
Key exclusion criteria (2 listed)
- Involvement in study design
- Unable to speak fluent English.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Qualitative themes and subthemes
Time frame: Day 1 (interview date) through study completion (1 year)
1 secondary outcome
Number of participants per stakeholder group
Time frame: Day 1
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 21 Oct 2026.
- Individual participant dataSharing planned: No. Qualitative interview data may include sensitive personal health, commercial, or regulatory information.
Related trials for Artificial Intelligence Perception and Attitude
Not yet recruitingObservational study
Assessing Knowledge, Attitudes, Trust, Perceived Usefulness, and Barriers to Using Artificial Intelligence (AI) in Gastroenterology Among Clinicians and Patients
AI and GIT
Not yet recruiting
The Effect of AI-Assisted Nursing Process Training on Nursing Process Competence, Perception and Attitudes Towards Artificial Intelligence in Nurses: A Randomized Controlled Study
Nursing Process Competence, Artificial Intelligence Perception and Attitude
Enrolling by invitationObservational study
Attitudes and Perceptions of Corresponding Authors From Top International Medical Journals Regarding the Use of Artificial Intelligence in the Scientific Process
Artificial Intelligence, Biomedical Research
Active, not recruiting
Effect of Perception-based Interventions on Public Acceptance of Using Large Language Models in Medicine
Large Language Models, Acceptability of Health Care
Recruiting
AI and VR Devices for Eating Behavior Rehabilitation
Eating Disorders, Anorexia Nervosa
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.
Dominic Furniss, DM MA MBBCh FRCS study chair
University of Oxford
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence Perception and Attitude, run by University of Oxford at 1 site in United Kingdom.
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 7 inclusion and 2 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: enrolling by invitation, as of the sponsor's last update on 21 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Qualitative themes and subthemes (time frame: Day 1 (interview date) through study completion (1 year)). 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 21 Oct 2026.