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Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

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Impact of using artificial intelligence as a second reader in breast screening including arbitration.

Lucy M Warren¹, Jenny Venton², Kenneth C Young^2,3

¹Royal Surrey NHS Foundation Trust, Guildford, UK. lucy.warren@nhs.net.

|March 11, 2026

Summary

Artificial intelligence (AI) in double-read breast screening is noninferior to human readers for sensitivity and specificity. AI offers workload benefits, but further development is needed for earlier cancer detection.

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

The integration of artificial intelligence (AI) into breast cancer screening workflows, particularly double-read systems with arbitration, requires thorough evaluation.
The impact of AI on diagnostic accuracy and workflow efficiency in breast screening remains an area of active research.

Purpose of the Study:

To assess the impact of incorporating AI into a double-read breast-screening workflow, including arbitration, on diagnostic performance and workload.
To determine if AI-assisted screening leads to earlier cancer detection compared to traditional methods.

Main Methods:

A retrospective study of 50,000 women from two NHS breast-screening centers with long-term follow-up.
Evaluation of 8,732 cases requiring arbitration, read by 22 human readers and compared against an AI tool.
Analysis of sensitivity, specificity, and workload implications of replacing the second human reader with AI.

Main Results:

Replacing the second human reader with AI in a double-read arbitration workflow was noninferior to two human readers in terms of sensitivity and specificity (P < 0.001).
The AI tool demonstrated a significant workload benefit.
Arbitration improved AI arm specificity by correcting false positives but also overruled AI recalls for some interval and next-round cancers.

Conclusions:

AI can be a viable alternative to a second human reader in double-read breast screening, offering comparable diagnostic performance and workload advantages.
Further AI tool development, focusing on explainability, is crucial for enhancing earlier cancer detection and minimizing missed diagnoses.