AI-Augmented Diagnostic Assessment With ENLIGHT Versus Independent Pathologist Review

Enrolling by invitation · Not applicable

Conditions studied: Cancer

In brief

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.

Key facts

Study ID
NCT07741058
Run by
Harvard Medical School (HMS and HSDM)
People needed
25
Starts
2026-07-01
Expected to finish
2026-08-01
Last updated by the study team
2026-08-03

Who can join

Age: any. Sex: any. Healthy volunteers: not accepted.

Where it is running

Full record on ClinicalTrials.gov

Trial information comes from ClinicalTrials.gov and is refreshed daily. TrialsForMe does not provide medical care and does not run the studies it lists.