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

28 luglio 2026 aggiornato da: 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.

Panoramica dello studio

Stato

Iscrizione su invito

Condizioni

Intervento / Trattamento

Descrizione dettagliata

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.

Tipo di studio

Interventistico

Iscrizione (Stimato)

25

Fase

  • Non applicabile

Contatti e Sedi

Questa sezione fornisce i recapiti di coloro che conducono lo studio e informazioni su dove viene condotto lo studio.

Luoghi di studio

  • Stati Uniti
    • Massachusetts
      • Boston, Massachusetts, Stati Uniti, 02115
        • Harvard Medical School,

Criteri di partecipazione

I ricercatori cercano persone che corrispondano a una certa descrizione, chiamata criteri di ammissibilità. Alcuni esempi di questi criteri sono le condizioni generali di salute di una persona o trattamenti precedenti.

Criteri di ammissibilità

Età idonea allo studio

  • Bambino
  • Adulto
  • Adulto più anziano

Accetta volontari sani

No

Descrizione

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

Piano di studio

Questa sezione fornisce i dettagli del piano di studio, compreso il modo in cui lo studio è progettato e ciò che lo studio sta misurando.

Come è strutturato lo studio?

Dettagli di progettazione

  • Scopo principale: Diagnostico
  • Assegnazione: Randomizzato
  • Modello interventistico: Assegnazione incrociata
  • Mascheramento: Quadruplicare

Armi e interventi

Gruppo di partecipanti / Arm
Intervento / Trattamento
Comparatore attivo: 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.
Comportamentale: 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.
Comparatore attivo: 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.
Comportamentale: 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.
Comparatore attivo: 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.
Comportamentale: 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.
Comparatore attivo: 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.
Comportamentale: 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.

Cosa sta misurando lo studio?

Misure di risultato primarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Diagnostic performance of cancers
Lasso di tempo: 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)

Misure di risultato secondarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Tempo per la diagnosi
Lasso di tempo: Periprocedurale (al momento della revisione delle diapositive)
Tempo medio (secondi per caso) richiesto per finalizzare una diagnosi.
Periprocedurale (al momento della revisione delle diapositive)
Variabilità inter-Observer
Lasso di tempo: Periprocedurale (al momento della revisione delle diapositive)
Accordo tra i clinici attraverso le condizioni, misurato utilizzando metriche di affidabilità inter-rater (ad es. Statistiche di Kappa).
Periprocedurale (al momento della revisione delle diapositive)
Beneficio netto dopo esposizione all'IA
Lasso di tempo: Periprocedurale (al momento della revisione delle diapositive)
Il cambiamento complessivo nell'accuratezza diagnostica attribuibile all'assistenza all'intelligenza artificiale.
Periprocedurale (al momento della revisione delle diapositive)
Clinician confidence level
Lasso di tempo: 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)

Collaboratori e investigatori

Qui è dove troverai le persone e le organizzazioni coinvolte in questo studio.

Sponsor

Harvard Medical School (HMS and HSDM)

Studiare le date dei record

Queste date tengono traccia dell'avanzamento della registrazione dello studio e dell'invio dei risultati di sintesi a ClinicalTrials.gov. I record degli studi e i risultati riportati vengono esaminati dalla National Library of Medicine (NLM) per assicurarsi che soddisfino specifici standard di controllo della qualità prima di essere pubblicati sul sito Web pubblico.

Studia le date principali

Inizio studio (Stimato)

1 luglio 2026

Completamento primario (Stimato)

1 agosto 2026

Completamento dello studio (Stimato)

1 agosto 2026

Date di iscrizione allo studio

Primo inviato

28 luglio 2026

Primo inviato che soddisfa i criteri di controllo qualità

28 luglio 2026

Primo Inserito (Effettivo)

3 agosto 2026

Aggiornamenti dei record di studio

Ultimo aggiornamento pubblicato (Effettivo)

3 agosto 2026

Ultimo aggiornamento inviato che soddisfa i criteri QC

28 luglio 2026

Ultimo verificato

1 luglio 2026

Maggiori informazioni

Termini relativi a questo studio

Parole chiave

Termini MeSH pertinenti aggiuntivi

Altri numeri di identificazione dello studio

  • ENLIGHT Study

Piano per i dati dei singoli partecipanti (IPD)

Hai intenzione di condividere i dati dei singoli partecipanti (IPD)?

NO

Informazioni su farmaci e dispositivi, documenti di studio

Studia un prodotto farmaceutico regolamentato dalla FDA degli Stati Uniti

No

Studia un dispositivo regolamentato dalla FDA degli Stati Uniti

No

Queste informazioni sono state recuperate direttamente dal sito web clinicaltrials.gov senza alcuna modifica. In caso di richieste di modifica, rimozione o aggiornamento dei dettagli dello studio, contattare register@clinicaltrials.gov. Non appena verrà implementata una modifica su clinicaltrials.gov, questa verrà aggiornata automaticamente anche sul nostro sito web .

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