AI-Augmented Diagnostic Assessment With ENLIGHT Versus Independent Pathologist Review
Public ClinicalTrials.gov record NCT07741058. Field values are reproduced from the official study page; the official ClinicalTrials.gov record remains the source of truth for eligibility, enrollment, and contact information.
Data is sourced from official ClinicalTrials.gov public API records. Always review the official ClinicalTrials.gov record for the latest information.
Brief summary
Reproduced verbatim from the official ClinicalTrials.gov record. Not medical advice.
This study will evaluate whether artificial intelligence (AI) can enhance clinicians' accuracy, efficiency, and confidence in distinguishing lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC) and kidney renal papillary cell carcinoma (KIRP) from kidney renal clear cell carcinoma (KIRC) using digitized pathology slides. These subtype classifications are routinely performed by pathologists but can be challenging and time-consuming, particularly in difficult cases. During the study, participating clinicians will review lung and kidney pathology slides under three different conditions: * Unaided Review: Diagnosis without AI assistance. * AI as Double-Check: The clinician first makes an independent diagnosis, after which the AI-generated diagnosis (prediction only or prediction with explanation) is revealed for review. * AI as First-Look: The AI-generated diagnosis (prediction only or prediction with explanation) is presented before the clinician begins the review. Clinicians will be randomly assigned to different review sequences to minimize potential order effects. This study design will enable us to assess the impact of AI assistance on diagnostic accuracy, interpretation time, and clinician confidence.
Study identification
- NCT ID
- NCT07741058
- Recruitment status
- Enrolling by invitation
- Study type
- Interventional
- Phase
- Not applicable
- Enrollment
- 25 participants
Conditions and interventions
Conditions
Interventions
- Unaided Review First, Then AI as Double-Check, Then AI as First-Look. Behavioral
- Unaided Review First, Then AI as First-Look, Then AI as Double-Check. Behavioral
- AI as Double-Check First, Then AI as First-Look, Then Unaided Review. Behavioral
- AI as First-Look First, Then AI as Double-Check, Then Unaided Review. Behavioral
Behavioral
Eligibility (public fields only)
- Age range
- Not listed
- Sex
- All
- Healthy volunteers
- Healthy volunteers not accepted
This page does not interpret eligibility. Detailed inclusion and exclusion criteria are on the official ClinicalTrials.gov record.
Study timeline
- Start date
- Jun 30, 2026
- Primary completion
- Jul 31, 2026
- Completion
- Jul 31, 2026
- Last update posted
- Aug 2, 2026
2026
United States locations
- U.S. sites
- 1
- U.S. states
- 1
- U.S. cities
- 1
| Facility | City | State | ZIP | Site status |
|---|---|---|---|---|
| Harvard Medical School, | Boston | Massachusetts | 02115 | — |
Site contact phone numbers, emails, and investigator names are intentionally not displayed here. Open the official ClinicalTrials.gov record for site contact information.
About this trial record page
- What this page shows
- Public field values for ClinicalTrials.gov record NCT07741058, including study identification, conditions, interventions, eligibility (age, sex, healthy volunteer), timeline, and U.S. site list.
- What this page does not do
- No medical advice, eligibility judgments, treatment recommendations, study quality scoring, or AI-generated medical summaries. No site contact phone numbers, emails, or investigator names.
- Where the data comes from
- Sourced from the official ClinicalTrials.gov public API. The official record is the source of truth.
- Last refresh
- Last update posted Aug 2, 2026 · Synced Sep 3, 2026
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Open the official record
The complete protocol, eligibility criteria, and contact information for NCT07741058 live on ClinicalTrials.gov.