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Artificial intelligence and digital pathology: clinical promise and deployment considerations.

Mark D Zarella¹, David S McClintock¹, Harsh Batra²

¹Mayo Clinic, Division of Computational Pathology and AI, Department of Laboratory Medicine and Pathology, Rochester, Minnesota, United States.

Journal of Medical Imaging (Bellingham, Wash.)

|August 2, 2023

Summary

Artificial intelligence (AI) offers quantitative support to anatomic pathology, enhancing diagnostics and workflows. Overcoming adoption barriers through stakeholder inclusion and reimbursement is key to realizing AI's clinical potential.

Keywords:

computational pathology digital pathology image analysis machine learning whole-slide imaging

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Area of Science:

Anatomic Pathology
Digital Pathology
Artificial Intelligence

Background:

Anatomic pathology traditionally relies on subjective interpretation of glass slides.
Digital pathology, utilizing whole-slide imaging, is essential for AI implementation.
AI offers quantitative, objective support to enhance diagnostic capabilities and clinical workflows.

Purpose of the Study:

To explore the role and potential of artificial intelligence in anatomic pathology.
To categorize existing AI models and their applications within the pathology workflow.
To identify barriers to AI adoption and suggest solutions for broader implementation.

Main Methods:

Review of existing artificial intelligence models in anatomic pathology.
Categorization of AI models based on their function and application.
Analysis of challenges and opportunities for AI integration in pathology practices.

Main Results:

AI models can provide operational support and improve diagnostic quality in pathology.
AI applications range from screening and triage to diagnostic assistance and virtual second opinions.
Numerous barriers hinder AI adoption, including technical, regulatory, and financial challenges.

Conclusions:

Artificial intelligence holds significant promise for transforming anatomic pathology.
Successful integration requires addressing barriers such as stakeholder engagement and reimbursement.
Further development and validation are crucial for widespread clinical adoption of AI in pathology.