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Augmented reality microscopy to bridge trust between AI and pathologists.

Sunil Badve1, George L Kumar2, Tobias Lang3

  • 1Emory University School of Medicine, Atlanta, GA, USA. sbadve@emory.edu.

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|May 12, 2025
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Summary
This summary is machine-generated.

Artificial intelligence (AI) in pathology improves diagnostic certainty for programmed cell death ligand 1 (PD-L1) testing. Pathologist-in-the-loop AI models enhance interobserver agreement and accuracy in clinical practice.

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Area of Science:

  • Computational pathology
  • Biomarker analysis
  • Digital health

Background:

  • Immunohistochemistry (IHC) biomarker evaluation faces challenges with subjective interpretation and variable scoring.
  • Diagnostic certainty is crucial for effective patient treatment and maximizing therapeutic benefits.

Purpose of the Study:

  • To develop and evaluate an AI model for programmed cell death ligand 1 (PD-L1) scoring using a pathologist-in-the-loop approach.
  • To assess the impact of AI assistance on interobserver variability and pathologist trust in IHC interpretation.

Main Methods:

  • A PD-L1 CPS AI Model was created through pathologist-in-the-loop finetuning of an IHC foundation model.
  • An augmented reality microscope (ARM) system integrated the AI model to evaluate difficult gastroesophageal biopsy regions.
  • Interobserver agreement and pathologist trust in AI outputs were assessed.

Main Results:

  • AI assistance improved case agreement between pathologists by 14% (77% vs 91%) and among 11 pathologists by 26% (43% vs 69%).
  • AI significantly increased the number of cases identified as PD-L1 positive (CPS ≥ 5) by all pathologists by 31%.
  • The study demonstrated increased diagnostic consistency and accuracy with AI-assisted IHC interpretation.

Conclusions:

  • Engaging pathologists in AI development and deployment is key for trustworthy AI in pathology.
  • AI-assisted IHC interpretation, particularly for PD-L1, offers a pathway to reduce diagnostic uncertainty and improve clinical adoption.
  • This approach provides a roadmap for integrating AI into routine pathology practice, enhancing diagnostic reliability.