Preterm BirthNCT07402668
Does AI Make Clinicians More Appropriately Confident? A Randomized Study in Preterm Birth Prediction
A study testing AI prediction (binary) and AI risk estimate (%) in people with Preterm Birth and Artificial Intelligence (AI) in Diagnosis, run by Copenhagen University Hospital, Rigshospitalet at 18 sites in Denmark.
Trial summary
The goal of this randomized questionnaire-based study is to evaluate how different presentations of artificial intelligence (AI) decision support influence clinical judgment among medical doctors working in obstetrics and gynecology when assessing the risk of spontaneous preterm birth using clinical case vignettes… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Preterm Birth, Artificial Intelligence (AI) in Diagnosis
- What is being tested?
- AI prediction (binary) and AI risk estimate (%)
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 125 participants
- Where
- 18 sitesDenmark
- Study dates
- 3 Feb 2026to Jul 2026
- Phase
- Interventional
- 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
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Medical doctors currently working in or training within the field of obstetrics and gynecology.
- Experience performing transvaginal cervical ultrasound examinations.
Who cannot join
- No prior experience performing transvaginal cervical ultrasound examinations.
Trial locations and contacts
18 sites in 1 country. Contact the study team or a site near you to ask about taking part.
18 sites in 1 country
Denmark18 sites, 18 recruiting
- Recruiting
South Jutland Hospital
Aabenraa, 6200
Emilie Pi F Sejer, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Aalborg University Hospital (Aalborg and Thisted)
Aalborg, 9000
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Aarhus University Hospital
Aarhus, 8200
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Copenhagen University Hospital, Rigshospitalet
Copenhagen, 2100
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Esbjerg Hospital
Esbjerg, 6700
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Gødstrup Regional Hospital
Gødstrup, 7400
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Herlev Hospital
Herlev, 2730
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Copenhagen University Hospital, North Zealand
Hillerød, 3400
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Region Hospital North Jutland
Hjørring, 9800
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Holbæk Hospital
Holbæk, 4300
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Horsens Regional Hospital
Horsens, 8700
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Hvidovre Hospital
Hvidovre, 2650
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Kolding Hospital
Kolding, 6000
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Odense University Hospital (Odense and Svendborg)
Odense, 5000
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Randers Regional Hospital
Randers, 8930
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Zealand University Hospital (Roskilde and Nykøbing F.)
Roskilde, 4000
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Slagelse Hospital
Slagelse, 4200
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
- Recruiting
Viborg Regional Hospital
Viborg, 8800
Emilie Pi F Sejer, MD, 0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk
About this study
The goal of this randomized questionnaire-based study is to evaluate how different presentations of artificial intelligence (AI) decision support influence clinical judgment among medical doctors working in obstetrics and gynecology when assessing the risk of spontaneous preterm birth using clinical case vignettes with cervical ultrasound images. The study specifically compares two AI presentation formats: a binary classification (preterm vs term birth) and an individualized risk estimate of preterm birth.
The main questions it aims to answer are:
Read more
- Which AI presentation format leads to better alignment between clinicians' confidence and decision accuracy (diagnostic calibration)?
- Do different AI presentation formats lead to helpful or harmful changes in clinical decisions?
Participants will complete an online questionnaire in which they review clinical cases, make diagnostic and management decisions, rate their diagnostic confidence before and after seeing the AI output, and report their trust in the AI.
- Being tested
- AI prediction (binary), AI risk estimate (%)
- Keywords
- Preterm birth, Premature birth, Diagnostic calibration, Diagnostic accuracy, Diagnostic confidence, Artificial intelligence
- Medical subject headings
- Premature Birth
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI prediction (binary)Behavioural
AI decision support based on cervical ultrasound providing a binary classification (preterm birth before 37 weeks or term birth) in addition to standard clinical information.
AI risk estimate (%)Behavioural
AI decision support based on cervical ultrasound providing an estimate of preterm birth risk (%) in addition to standard clinical information.
| Study group | Role | What they receive |
|---|---|---|
| AI prediction | Experimental | The participants receive a binary AI prediction (preterm or term birth) Receives: Behavioral: AI prediction (binary) |
| AI risk estimate | Experimental | The participants receive an AI risk estimate of preterm birth (%) Receives: Behavioral: AI risk estimate (%) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clinician diagnostic calibration (accuracy-confidence alignment) after AI exposure.
Agreement between post-AI decision correctness (0/1) and post-AI confidence rating (0-10) will be quantified using the Brier score. Confidence will be rescaled to 0-1 and squared differences between confidence and correctness will be averaged across cases to produce a participant-level score. Lower scores indicate better diagnostic calibration. Results will be compared between randomized arms.
Measured: Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Secondary outcomes (4)
Helpful switch rate and harmful switch rate.
Proportion of cases with helpful and harmful switches calculated for each participant and compared between study arms.
Helpful switch = incorrect pre-AI decision changing to correct post-AI decision.
Harmful switch = correct pre-AI decision changing to incorrect post-AI decision.
Measured: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Change in decision accuracy, confidence, and diagnostic calibration from pre-AI to post-AI.
Within-participant change from pre-AI to post-AI in decision accuracy (proportion of correct decisions), confidence rating, and diagnostic calibration. Differences will be compared between randomized arms and stratified by AI correctness.
Measured: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Association between self-rated trust in AI and behavioral reliance on AI.
Self-rated trust in the AI output will be measured using a numeric rating scale (0-10) after AI exposure for each case. Behavioral reliance will be quantified as the proportion of post-AI decisions concordant with the AI output. The relationship between trust ratings and behavioral reliance, including concordance when the AI is correct and incorrect, will be evaluated at the participant level and compared between randomized arms.
Measured: Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Follow-up cervical ultrasound planning.
Proportion of cases in which clinicians plan an additional cervical ultrasound (yes/no), summarized per participant and compared pre-post AI and between randomized arms.
Measured: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Feb 2026
- Study start3 Feb 2026
- Primary completionJul 2026 (estimated)
- Study completionJul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Preterm BirthStudies of the same condition, open and under way.
- More trials run by Copenhagen University Hospital, RigshospitaletThe 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 125 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Copenhagen University Hospital, Rigshospitalet
- Sponsor type
- University, hospital or other
- Collaborators
- Technical University of Denmark, The Foundation of 17.12.1981, Department of Computer Science, University of Copenhagen, Denmark, Copenhagen Academy for Medical Education and Simulation
- Responsible party
- Principal investigator, Emilie Pi Fogtmann Sejer, Copenhagen University Hospital, Rigshospitalet
- Investigators
- Martin G Tolsgaard, MD, PhD, DMSc, Study chair, Department of Obstetrics and Gynecology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07402668
- Sponsor's protocol ID
- P-2024-18108
- Status last verified
- Jun 2026
- First submitted
- 27 Jan 2026
- Last updated
- 1 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data are not planned to be publicly shared. Aggregate results will be reported. De-identified data may be made available upon reasonable request and subject to institutional policies and applicable data protection regulations.
Study at a glance
From the registry record, last updated by the sponsor on 1 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Does AI Make Clinicians More Appropriately Confident? A Randomized Study in Preterm Birth Prediction
- ClinicalTrials.gov ID
- NCT07402668
- Acronym / sponsor ID
- P-2024-18108
- Keywords
- Preterm birth, Premature birth, Diagnostic calibration, Diagnostic accuracy, Diagnostic confidence, Artificial intelligence
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 125 participants (estimated)
- Lead sponsor
- Copenhagen University Hospital, RigshospitaletUniversity, hospital or other
- Collaborators
- Technical University of Denmark, The Foundation of 17.12.1981, Department of Computer Science, University of Copenhagen, Denmark, Copenhagen Academy for Medical Education and Simulation
- Study start
- 3 Feb 2026 (actual)
- Primary completion
- Jul 2026 (estimated)
- Study completion
- Jul 2026 (estimated)
- First posted
- 11 Feb 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.
The goal of this randomized questionnaire-based study is to evaluate how different presentations of artificial intelligence (AI) decision support influence clinical judgment among medical doctors working in obstetrics and gynecology when assessing the risk of spontaneous preterm birth using clinical case vignettes with cervical ultrasound images. The study specifically compares two AI presentation formats: a binary classification (preterm vs term birth) and an individualized risk estimate of preterm birth.
The main questions it aims to answer are:
- Which AI presentation format leads to better alignment between clinicians' confidence and decision accuracy (diagnostic calibration)?
- Do different AI presentation formats lead to helpful or harmful changes in clinical decisions?
Participants will complete an online questionnaire in which they review clinical cases, make diagnostic and management decisions, rate their diagnostic confidence before and after seeing the AI output, and report their trust in the AI.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI prediction | Experimental | Behavioral: AI prediction (binary)The participants receive a binary AI prediction (preterm or term birth) |
| AI risk estimate | Experimental | Behavioral: AI risk estimate (%)The participants receive an AI risk estimate of preterm birth (%) |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (2 listed)
- Medical doctors currently working in or training within the field of obstetrics and gynecology.
- Experience performing transvaginal cervical ultrasound examinations.
Key exclusion criteria (1 listed)
- No prior experience performing transvaginal cervical ultrasound examinations.
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 |
|---|---|---|
| AI prediction (binary)AI decision support based on cervical ultrasound providing a binary classification (preterm birth before 37 weeks or term birth) in addition to standard clinical information. | Behavioural | AI prediction |
| AI risk estimate (%)AI decision support based on cervical ultrasound providing an estimate of preterm birth risk (%) in addition to standard clinical information. | Behavioural | AI risk estimate |
Primary outcome
Clinician diagnostic calibration (accuracy-confidence alignment) after AI exposure.
Time frame: Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Agreement between post-AI decision correctness (0/1) and post-AI confidence rating (0-10) will be quantified using the Brier score. Confidence will be rescaled to 0-1 and squared differences between confidence and correctness will be averaged across cases to produce a participant-level score. Lower scores indicate better diagnostic calibration. Results will be compared between randomized arms.
4 secondary outcomes
Helpful switch rate and harmful switch rate.
Time frame: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Change in decision accuracy, confidence, and diagnostic calibration from pre-AI to post-AI.
Time frame: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Association between self-rated trust in AI and behavioral reliance on AI.
Time frame: Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Follow-up cervical ultrasound planning.
Time frame: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
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 1 Jul 2026.
- Individual participant dataSharing planned: No. Individual participant data are not planned to be publicly shared. Aggregate results will be reported. De-identified data may be made available upon reasonable request and subject to institutional policies and applicable data protection regulations.
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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.
Martin G Tolsgaard, MD, PhD, DMSc study chair
Department of Obstetrics and Gynecology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
18 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI prediction (binary) and AI risk estimate (%) in people with Preterm Birth and Artificial Intelligence (AI) in Diagnosis, run by Copenhagen University Hospital, Rigshospitalet at 18 sites in Denmark.
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 2 inclusion and 1 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: looking for participants now, as of the sponsor's last update on 1 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 18 sites in Denmark. 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?
Contact Emilie Pi F Sejer, MD (0045 28890690, emilie.pi.fogtmann.sejer.01@regionh.dk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinician diagnostic calibration (accuracy-confidence alignment) after AI exposure. (time frame: Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).). 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 1 Jul 2026.