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Computational Cytology: Lessons Learned from Pap Test Computer-Assisted Screening.

Madelyn Lew¹, David C Wilbur², Liron Pantanowitz³

¹Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA, lewm@med.umich.edu.

Acta Cytologica

|July 23, 2020

Summary

This summary is machine-generated.

Lessons from automated Pap test screening can guide the clinical integration of artificial intelligence (AI) in computational cytology. Cytologists can leverage their experience to optimize AI development and implementation in pathology.

Keywords:

Artificial intelligence Computational cytology Computer-assisted screening Cytology Pap test Screening

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Area of Science:

Computational Cytology
Artificial Intelligence in Pathology

Background:

Rapid advancements in computational cytology and AI necessitate learning from past experiences.
The development of computer-assisted Pap test screening offers valuable insights for current technological integration.

Purpose of the Study:

To reflect on the lessons learned from decades of computer-assisted Pap test screening development.
To optimize the application of AI in clinical cytology practice.

Main Methods:

Historical analysis of automated screening technologies in cytology.
Examination of factors influencing the adoption of new technologies in cytopathology laboratories.

Main Results:

Early automated screening aimed to address Pap test backlogs and diagnostic errors.
Successful implementation depended on viable use cases, improved outcomes, and workflow integration.
Factors like FDA oversight, reimbursement, and user acceptance influenced commercial success.

Conclusions:

Healthcare technology adoption involves successes and failures, requiring perseverance.
Optimizing workflow, improving accuracy, and gaining regulatory/financial approval are key to widespread adoption.
Cytologists' experience with automated screening positions them to guide AI development and utilization in pathology.