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AI-Augmented Diagnostic Assessment With ENLIGHT Versus Independent Pathologist Review (ENLIGHT)

2026. július 28. frissítette: Kun-Hsing Yu, Harvard Medical School (HMS and HSDM)

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.

A tanulmány áttekintése

Állapot

Jelentkezés meghívóval

Körülmények

Beavatkozás / kezelés

Részletes leírás

This study aims to evaluate the effect of artificial intelligence (AI) assistance on clinicians' diagnostic performance 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 hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). ENLIGHT (Explainable Neoplasm Learning In Grounded Histology Terms) will serve as the AI system under evaluation. This is a single-session, within-reader, between-case study in which each reader evaluates distinct sets of cases under all study conditions.

The study includes three diagnostic blocks: Block X, in which WSIs are reviewed without AI assistance; Block Y1, in which clinicians make an initial diagnosis before viewing the AI output as a double-check; and Block Y2, in which the AI output is displayed before clinicians begin their review as a first-look aid. Within each AI-assisted block, the prediction-only and prediction-with-explanation sub-blocks are presented in randomized order.

Each participating pathologist will review up to 400 de-identified WSIs (up to 200 lung cancer and up to 200 kidney cancer cases). Readers will be randomly assigned to one of four study arms that differ only in the order in which Blocks X, Y1, and Y2 are completed. For each reader, distinct WSIs will be randomly assigned to the diagnostic conditions so that no WSI is reviewed more than once by the same reader.

  • Arm 1 (X -> Y1 -> Y2): Clinicians first complete Block X (Unaided Review), followed by Block Y1 (AI as Double-Check) and then Block Y2 (AI as First-Look).
  • Arm 2 (X -> Y2 -> Y1): Clinicians first complete Block X (Unaided Review), followed by Block Y2 (AI as First-Look) and then Block Y1 (AI as Double-Check).
  • Arm 3 (Y1 -> Y2 -> X): Clinicians first complete Block Y1 (AI as Double-Check), followed by Block Y2 (AI as First-Look), and then Block X (Unaided Review).
  • Arm 4 (Y2 -> Y1 -> X): Clinicians first complete Block Y2 (AI as First-Look), followed by Block Y1 (AI as Double-Check), and then Block X (Unaided Review).

For each case, diagnostic accuracy, time to diagnosis, and diagnostic confidence will be recorded. No reader will review the same WSI under more than one condition, thereby eliminating within-reader recall bias. In parallel, the ENLIGHT model will independently generate diagnostic predictions for all WSIs to enable direct benchmarking of AI performance against pathologists and to evaluate the impact of different AI-assisted workflows on diagnostic performance.

Tanulmány típusa

Beavatkozó

Beiratkozás (Becsült)

25

Fázis

  • Nem alkalmazható

Kapcsolatok és helyek

Ez a rész a vizsgálatot végzők elérhetőségeit, valamint a vizsgálat lefolytatásának helyére vonatkozó információkat tartalmazza.

Tanulmányi helyek

  • Egyesült Államok
    • Massachusetts
      • Boston, Massachusetts, Egyesült Államok, 02115
        • Harvard Medical School,

Részvételi kritériumok

A kutatók olyan embereket keresnek, akik megfelelnek egy bizonyos leírásnak, az úgynevezett jogosultsági kritériumoknak. Néhány példa ezekre a kritériumokra a személy általános egészségi állapota vagy a korábbi kezelések.

Jogosultsági kritériumok

Tanulmányozható életkorok

  • Gyermek
  • Felnőtt
  • Idősebb felnőtt

Egészséges önkénteseket fogad

Nem

Leírás

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

Tanulási terv

Ez a rész a vizsgálati terv részleteit tartalmazza, beleértve a vizsgálat megtervezését és a vizsgálat mérését.

Hogyan készül a tanulmány?

Tervezési részletek

  • Elsődleges cél: Diagnosztikai
  • Kiosztás: Véletlenszerűsített
  • Beavatkozó modell: Crossover kiosztás
  • Maszkolás: Négyszeres

Fegyverek és beavatkozások

Résztvevő csoport / kar
Beavatkozás / kezelés
Aktív összehasonlító: 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, 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.
Viselkedési: 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.
Aktív összehasonlító: 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, 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.
Viselkedési: 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.
Aktív összehasonlító: AI as Double-Check Review 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, 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.
Viselkedési: 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.
Aktív összehasonlító: AI as First-Look Review 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, 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.
Viselkedési: 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.

Mit mér a tanulmány?

Elsődleges eredményintézkedések

Eredménymérő
Intézkedés leírása
Időkeret
Diagnostic performance of cancers
Időkeret: Periprocedural (at the time of slide review)
Performance of clinicians (unaided and AI-assisted) for distinguishing LUAD- LUSC and distinguishing KIRP-KIRC, measured in accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1.
Periprocedural (at the time of slide review)

Másodlagos eredményintézkedések

Eredménymérő
Intézkedés leírása
Időkeret
Ideje a diagnózishoz
Időkeret: Periprocedural (a diavetítés idején)
A diagnózis véglegesítéséhez szükséges átlagos idő (esetenként másodpercenként).
Periprocedural (a diavetítés idején)
Megfigyelésközi variabilitás
Időkeret: Periprocedural (a diavetítés idején)
Megállapodás a klinikusok között a körülmények között, az érték-érték-megbízhatósági mutatókkal (például a kappa statisztikák).
Periprocedural (a diavetítés idején)
Nettó haszon az AI expozíció után
Időkeret: Periprocedural (a diavetítés idején)
A diagnosztikai pontosság általános változása az AI -támogatásnak tulajdonítható.
Periprocedural (a diavetítés idején)
Clinician confidence level
Időkeret: Periprocedural (at the time of slide review)
Self-reported diagnostic confidence recorded for each case. Scale: 5 - Absolutely Certain; 4 - Mostly Certain; 3 - Unsure; 2 - Very Doubtful; 1 - Random Guess; With 5 being the highest confidence score and 1 being the lowest.
Periprocedural (at the time of slide review)

Együttműködők és nyomozók

Itt találhatja meg a tanulmányban érintett személyeket és szervezeteket.

Szponzor

Harvard Medical School (HMS and HSDM)

Tanulmányi rekorddátumok

Ezek a dátumok nyomon követik a ClinicalTrials.gov webhelyre benyújtott vizsgálati rekordok és összefoglaló eredmények benyújtásának folyamatát. A vizsgálati feljegyzéseket és a jelentett eredményeket a Nemzeti Orvostudományi Könyvtár (NLM) felülvizsgálja, hogy megbizonyosodjon arról, hogy megfelelnek-e az adott minőség-ellenőrzési szabványoknak, mielőtt közzéteszik őket a nyilvános weboldalon.

Tanulmány főbb dátumok

Tanulmány kezdete (Becsült)

2026. július 1.

Elsődleges befejezés (Becsült)

2026. augusztus 1.

A tanulmány befejezése (Becsült)

2026. augusztus 1.

Tanulmányi regisztráció dátumai

Először benyújtva

2026. július 28.

Először nyújtották be, amely megfelel a minőségbiztosítási kritériumoknak

2026. július 28.

Első közzététel (Tényleges)

2026. augusztus 3.

Tanulmányi rekordok frissítései

Utolsó frissítés közzétéve (Tényleges)

2026. augusztus 3.

Az utolsó frissítés elküldve, amely megfelel a minőségbiztosítási kritériumoknak

2026. július 28.

Utolsó ellenőrzés

2026. július 1.

Több információ

A tanulmányhoz kapcsolódó kifejezések

Kulcsszavak

További vonatkozó MeSH feltételek

Egyéb vizsgálati azonosító számok

  • ENLIGHT Study

Terv az egyéni résztvevői adatokhoz (IPD)

Tervezi megosztani az egyéni résztvevői adatokat (IPD)?

NEM

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Egy amerikai FDA által szabályozott gyógyszerkészítményt tanulmányoz

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Nem

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