Clinical trial · Interventional
AI-Augmented Diagnostic Assessment With ENLIGHT Versus Independent Pathologist Review
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cancer | Malignant Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (4)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI as Double-Check First, Then AI as First-Look, Then Unaided Review. | Behavioral | — | UNRESOLVED |
| AI as First-Look First, Then AI as Double-Check, Then Unaided Review. | Behavioral | — | UNRESOLVED |
| Unaided Review First, Then AI as Double-Check, Then AI as First-Look. | Behavioral | — | UNRESOLVED |
| Unaided Review First, Then AI as First-Look, Then AI as Double-Check. | Behavioral | — | UNRESOLVED |
Design
Arms and outcomes
Arms (4)
- type
- ACTIVE_COMPARATOR
- label
- Unaided Review First, Then AI as Double-Check, Then AI as First-Look.
- description
- Readers first complete Block X (Unaided) on their assigned subset SX. They then complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check). Within Block Y1, the order of SY1a and SY1b is randomized. They then complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look). Within Block Y2, the order of SY2a and SY2b is randomized. For each reader, each of the five subsets (SX, SY1a, SY1b, SY2a, and SY2b) comprises up to 80 slides: up to 40 slides from LUAD-LUSC and up to 40 slides from KIRP-KIRC.
- interventionNames
- Behavioral: Unaided Review First, Then AI as Double-Check, Then AI as First-Look.
- type
- ACTIVE_COMPARATOR
- label
- Unaided Review First, Then AI as First-Look, Then AI as Double-Check.
- description
- Readers first complete Block X (Unaided) on their assigned subset SX. They then complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look). Within Block Y2, the order of SY2a and SY2b is randomized. They then complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check). Within Block Y1, the order of SY1a and SY1b is randomized. For each reader, each of the five subsets (SX, SY1a, SY1b, SY2a, and SY2b) comprises up to 80 slides: up to 40 slides from LUAD-LUSC and up to 40 slides from KIRP-KIRC.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria for Pathology Slides (i.e., Cases): * Hematoxylin and eosin (H\&E)-stained pathology slides * Final diagnosis confirmed through molecular testing in conjunction with expert pathology evaluation Exclusion Criteria for Pathology Slides (i.e., Cases): * Poor-quality or unreadable slides * Cases used in AI training Inclusion Criteria for Readers (i.e., Participants): * Board-certified or board-eligible pathologists * Willingness to complete both unaided and AI-assisted review sessions
References
Publications (0)
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