AI-Augmented Diagnostic Assessment With ENLIGHT Versus Independent Pathologist Review

Summary

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

• Cancer

Interventions

BEHAVIORAL

Unaided Review First, Then AI as Double-Check, Then AI as First-Look.

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, SX, SY1a, SY1b, SY2a, and SY2b are disjoint.

BEHAVIORAL

Unaided Review First, Then AI as First-Look, Then AI as Double-Check.

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, SX, SY1a, SY1b, SY2a, and SY2b are disjoint.

BEHAVIORAL

AI as Double-Check First, Then AI as First-Look, Then Unaided Review.

Readers first 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. Then readers complete Block X (Unaided) on their assigned subset SX. For each reader, SX, SY1a, SY1b, SY2a, and SY2b are disjoint.

BEHAVIORAL

AI as First-Look First, Then AI as Double-Check, Then Unaided Review.

Readers first 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. Then readers complete Block X (Unaided) on their assigned subset SX. For each reader, SX, SY1a, SY1b, SY2a, and SY2b are disjoint.

Sponsors & Collaborators

• Harvard Medical School (HMS and HSDM)

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

DIAGNOSTIC

Masking

QUADRUPLE

Model

CROSSOVER

Eligibility

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-07-31

Primary Completion

2026-08-31

Completion

2026-08-31

Countries

• United States

Study Locations