Literature DB >> 31957101

The cytopathologist's role in developing and evaluating artificial intelligence in cytopathology practice.

Ewen D McAlpine1, Pamela Michelow1.   

Abstract

Artificial intelligence (AI) technologies have the potential to transform cytopathology practice, and it is important for cytopathologists to embrace this and place themselves at the forefront of implementing these technologies in cytopathology. This review illustrates an archetypal AI workflow from project conception to implementation in a diagnostic setting and illustrates the cytopathologist's role and level of involvement at each stage of the process. Cytopathologists need to develop and maintain a basic understanding of AI, drive decisions regarding the development and implementation of AI in cytopathology, participate in the generation of datasets used to train and evaluate AI algorithms, understand how the performance of these algorithms is assessed, participate in the validation of these algorithms (either at a regulatory level or in the laboratory setting), and ensure continuous quality assurance of algorithms deployed in a diagnostic setting. In addition, cytopathologists should ensure that these algorithms are developed, trained, tested and deployed in an ethical manner. Cytopathologists need to become informed consumers of these AI algorithms by understanding their workings and limitations, how their performance is assessed and how to validate and verify their output in clinical practice.
© 2020 John Wiley & Sons Ltd.

Keywords:  artificial intelligence; cytopathology; digital pathology

Year:  2020        PMID: 31957101     DOI: 10.1111/cyt.12799

Source DB:  PubMed          Journal:  Cytopathology        ISSN: 0956-5507            Impact factor:   2.073


  3 in total

Review 1.  Artificial intelligence and computational pathology.

Authors:  Miao Cui; David Y Zhang
Journal:  Lab Invest       Date:  2021-01-16       Impact factor: 5.662

2.  Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy.

Authors:  Christof A Bertram; Marc Aubreville; Taryn A Donovan; Alexander Bartel; Frauke Wilm; Christian Marzahl; Charles-Antoine Assenmacher; Kathrin Becker; Mark Bennett; Sarah Corner; Brieuc Cossic; Daniela Denk; Martina Dettwiler; Beatriz Garcia Gonzalez; Corinne Gurtner; Ann-Kathrin Haverkamp; Annabelle Heier; Annika Lehmbecker; Sophie Merz; Erica L Noland; Stephanie Plog; Anja Schmidt; Franziska Sebastian; Dodd G Sledge; Rebecca C Smedley; Marco Tecilla; Tuddow Thaiwong; Andrea Fuchs-Baumgartinger; Donald J Meuten; Katharina Breininger; Matti Kiupel; Andreas Maier; Robert Klopfleisch
Journal:  Vet Pathol       Date:  2021-12-30       Impact factor: 2.221

3.  Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study.

Authors:  Hong Jin; Xinyan Fu; Xinyi Cao; Mingxia Sun; Xiaofen Wang; Yuhong Zhong; Suwen Yang; Chao Qi; Bo Peng; Xin He; Fei He; Yongfang Jiang; Haiyan Gao; Shun Li; Zhen Huang; Qiang Li; Fengqi Fang; Jun Zhang
Journal:  J Med Syst       Date:  2020-09-07       Impact factor: 4.460

  3 in total

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