Ignite Creation Date:
2026-03-26 @ 3:15 PM
Ignite Modification Date:
2026-03-31 @ 12:25 AM
Study NCT ID:
NCT07402668
Status:
RECRUITING
Last Update Posted:
2026-02-17
First Post:
2026-01-27
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Does AI Make Clinicians More Appropriately Confident? A Randomized Study in Preterm Birth Prediction
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D047928', 'term': 'Premature Birth'}], 'ancestors': [{'id': 'D007752', 'term': 'Obstetric Labor, Premature'}, {'id': 'D007744', 'term': 'Obstetric Labor Complications'}, {'id': 'D011248', 'term': 'Pregnancy Complications'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['PARTICIPANT'], 'maskingDescription': 'Participants (clinicians) are blinded to randomized allocation and are unaware that different versions of the AI decision support are being compared. They view only the AI output presented within their assigned condition. No independent outcome assessors are involved. Outcomes are derived using pre-specified, objective scoring rules.'}, 'primaryPurpose': 'OTHER', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 125}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2026-02-03', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2026-03', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-13', 'studyFirstSubmitDate': '2026-01-27', 'studyFirstSubmitQcDate': '2026-02-03', 'lastUpdatePostDateStruct': {'date': '2026-02-17', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-03', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Clinician diagnostic calibration (accuracy-confidence alignment) after AI exposure.', 'timeFrame': 'Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Helpful switch rate and harmful switch rate.', 'timeFrame': 'Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).', 'description': 'Proportion of cases with helpful and harmful switches calculated for each participant and compared between study arms.\n\nHelpful switch = incorrect pre-AI decision changing to correct post-AI decision.\n\nHarmful switch = correct pre-AI decision changing to incorrect post-AI decision.'}, {'measure': 'Change in decision accuracy, confidence, and diagnostic calibration from pre-AI to post-AI.', 'timeFrame': 'Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).', 'description': '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.'}, {'measure': 'Association between self-rated trust in AI and behavioral reliance on AI.', 'timeFrame': 'Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).', 'description': '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.'}, {'measure': 'Follow-up cervical ultrasound planning.', 'timeFrame': 'Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).', 'description': '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.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Preterm birth', 'Premature birth', 'Diagnostic calibration', 'Diagnostic accuracy', 'Diagnostic confidence', 'Artificial intelligence'], 'conditions': ['Preterm Birth', 'Artificial Intelligence (AI) in Diagnosis']}, 'descriptionModule': {'briefSummary': "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.\n\nThe main questions it aims to answer are:\n\n* Which AI presentation format leads to better alignment between clinicians' confidence and decision accuracy (diagnostic calibration)?\n* Do different AI presentation formats lead to helpful or harmful changes in clinical decisions?\n\nParticipants 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Medical doctors currently working in or training within the field of obstetrics and gynecology.\n* Experience performing transvaginal cervical ultrasound examinations.\n\nExclusion Criteria:\n\n\\- No prior experience performing transvaginal cervical ultrasound examinations.'}, 'identificationModule': {'nctId': 'NCT07402668', 'briefTitle': 'Does AI Make Clinicians More Appropriately Confident? A Randomized Study in Preterm Birth Prediction', 'organization': {'class': 'OTHER', 'fullName': 'Rigshospitalet, Denmark'}, 'officialTitle': 'Does AI Make Clinicians More Appropriately Confident? A Randomized Study in Preterm Birth Prediction', 'orgStudyIdInfo': {'id': 'P-2024-18108'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'AI prediction', 'description': 'The participants receive a binary AI prediction (preterm or term birth)', 'interventionNames': ['Behavioral: AI prediction (binary)']}, {'type': 'EXPERIMENTAL', 'label': 'AI risk estimate', 'description': 'The participants receive an AI risk estimate of preterm birth (%)', 'interventionNames': ['Behavioral: AI risk estimate (%)']}], 'interventions': [{'name': 'AI prediction (binary)', 'type': 'BEHAVIORAL', 'description': '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.', 'armGroupLabels': ['AI prediction']}, {'name': 'AI risk estimate (%)', 'type': 'BEHAVIORAL', 'description': 'AI decision support based on cervical ultrasound providing an estimate of preterm birth risk (%) in addition to standard clinical information.', 'armGroupLabels': ['AI risk estimate']}]}, 'contactsLocationsModule': {'locations': [{'zip': '2100', 'city': 'Copenhagen', 'status': 'NOT_YET_RECRUITING', 'country': 'Denmark', 'contacts': [{'name': 'Emilie Pi F Sejer, MD', 'role': 'CONTACT', 'email': 'emilie.pi.fogtmann.sejer.01@regionh.dk', 'phone': '0045 28890690'}], 'facility': 'Copenhagen University Hospital, Rigshospitalet', 'geoPoint': {'lat': 55.67594, 'lon': 12.56553}}, {'zip': '2730', 'city': 'Herlev', 'status': 'RECRUITING', 'country': 'Denmark', 'contacts': [{'name': 'Emilie Pi F Sejer, MD', 'role': 'CONTACT', 'email': 'emilie.pi.fogtmann.sejer.01@regionh.dk', 'phone': '0045 28890690'}], 'facility': 'Herlev Hospital', 'geoPoint': {'lat': 55.72366, 'lon': 12.43998}}, {'zip': '3400', 'city': 'Hillerød', 'status': 'RECRUITING', 'country': 'Denmark', 'contacts': [{'name': 'Emilie Pi F Sejer, MD', 'role': 'CONTACT', 'email': 'emilie.pi.fogtmann.sejer.01@regionh.dk', 'phone': '0045 28890690'}], 'facility': 'Copenhagen University Hospital, North Zealand', 'geoPoint': {'lat': 55.92791, 'lon': 12.30081}}, {'zip': '4300', 'city': 'Holbæk', 'status': 'NOT_YET_RECRUITING', 'country': 'Denmark', 'contacts': [{'name': 'Emilie Pi F Sejer, MD', 'role': 'CONTACT', 'email': 'emilie.pi.fogtmann.sejer.01@regionh.dk', 'phone': '0045 28890690'}], 'facility': 'Holbæk Hospital', 'geoPoint': {'lat': 55.7175, 'lon': 11.71279}}, {'zip': '2650', 'city': 'Hvidovre', 'status': 'NOT_YET_RECRUITING', 'country': 'Denmark', 'contacts': [{'name': 'Emilie Pi F Sejer, MD', 'role': 'CONTACT', 'email': 'emilie.pi.fogtmann.sejer.01@regionh.dk', 'phone': '0045 28890690'}], 'facility': 'Hvidovre Hospital', 'geoPoint': {'lat': 55.64297, 'lon': 12.47708}}, {'zip': '4000', 'city': 'Roskilde', 'status': 'RECRUITING', 'country': 'Denmark', 'contacts': [{'name': 'Emilie Pi F Sejer, MD', 'role': 'CONTACT', 'email': 'emilie.pi.fogtmann.sejer.01@regionh.dk', 'phone': '0045 28890690'}], 'facility': 'Zealand University Hospital, Roskilde', 'geoPoint': {'lat': 55.64152, 'lon': 12.08035}}, {'zip': '4200', 'city': 'Slagelse', 'status': 'NOT_YET_RECRUITING', 'country': 'Denmark', 'contacts': [{'name': 'Emilie Pi F Sejer, MD', 'role': 'CONTACT', 'email': 'emilie.pi.fogtmann.sejer.01@regionh.dk', 'phone': '0045 28890690'}], 'facility': 'Slagelse Hospital', 'geoPoint': {'lat': 55.40276, 'lon': 11.35459}}], 'centralContacts': [{'name': 'Emilie Pi F Sejer, MD', 'role': 'CONTACT', 'email': 'emilie.pi.fogtmann.sejer.01@regionh.dk', 'phone': '0045 28890690'}], 'overallOfficials': [{'name': 'Martin G Tolsgaard, MD, PhD, DMSc', 'role': 'STUDY_CHAIR', 'affiliation': 'Department of Obstetrics and Gynecology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': '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.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Rigshospitalet, Denmark', 'class': 'OTHER'}, 'collaborators': [{'name': 'Technical University of Denmark (DTU)', 'class': 'UNKNOWN'}, {'name': 'The Foundation of 17.12.1981', 'class': 'OTHER'}, {'name': 'Department of Computer Science, University of Copenhagen, Denmark', 'class': 'UNKNOWN'}, {'name': 'Copenhagen Academy for Medical Education and Simulation', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Emilie Pi Fogtmann Sejer', 'investigatorAffiliation': 'Rigshospitalet, Denmark'}}}}