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Bridging the Clinical-Computational Transparency Gap in Digital Pathology.

Qiangqiang Gu¹, Ankush Patel¹, Matthew G Hanna²

¹From the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Gu, Patel, Garcia, Zarella, McClintock, Hart).

Archives of Pathology & Laboratory Medicine

|June 13, 2024

Summary

Bridging the gap between pathologists and developers is key for computational pathology. Enhanced understanding of computational methods and clinical needs will improve computer-aided diagnostic (CAD) tools and patient care.

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Area of Science:

Computational pathology
Medical informatics
Digital pathology

Background:

Computational pathology integrates computational analysis with clinical pathology to enhance diagnostics and productivity.
Communication barriers between pathologists and developers impede the full potential of computational pathology tools.
Existing computer-aided diagnostic (CAD) tools show promise, with some gaining regulatory approval.

Purpose of the Study:

To propose a standardized framework to improve mutual understanding between pathologists and developers.
To enhance the development and clinical application of computer-aided diagnostic (CAD) tools.
To foster better comprehension of clinical objectives and computational methodologies.

Main Methods:

Defining pivotal roles for pathologists and computer scientists in the CAD development lifecycle.
Advocating for pathologists to understand computational terminologies, processes, and limitations.
Encouraging computer scientists to comprehend clinical use cases and avoid clinically irrelevant metrics.

Main Results:

Improved understanding of machine learning models among pathologists is essential to prevent misuse and misinterpretation.
There is a need for more accurate performance representation of algorithms compared to pathologist benchmarks.
Addressing communication gaps can lead to more effective CAD tool development and implementation.

Conclusions:

A comprehensive grasp of both computational and clinical paradigms is vital to bridge the translational gap in computational pathology.
Mutual comprehension between disciplines will enhance disease diagnosis accuracy and efficiency.
Improved collaboration ultimately benefits patient care through advanced diagnostic capabilities.