Artificial Intelligence (AI) in Pathology – A Summary and Challenges

Artificial Intelligence (AI) in Pathology – A Summary and Challenges

Saagar S Kulkarni

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Archana Buch

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Rohan Kulkarni

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Artificial Intelligence (AI) in Pathology – A Summary and Challenges

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Abstract

This bibliographic study covers Artificial Intelligence (AI)theory and its applications from the healthcare field and in particular from the discipline of pathology. This review includes basics of AI, supervised and unsupervised machine learning (ML), various supervised ML algorithms, and their applications in healthcare and pathology. Digital Pathology with Deep Machine Learning is more advantageous over traditional pathology that is based on ‘physical slide on a physical microscope’. However, various implementation challenges of cost, data quality, multicenter validation, bias, and regulatory approval issues for AI in clinical practice still remain, which are also described in this study.

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No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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How to Cite This Article

Saagar S Kulkarni. 2021. \u201cArtificial Intelligence (AI) in Pathology – A Summary and Challenges\u201d. Global Journal of Medical Research - K: Interdisciplinary GJMR-K Volume 21 (GJMR Volume 21 Issue K2).

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GJMR Volume 21 Issue K2

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Crossref Journal DOI 10.17406/gjmra

Print ISSN 0975-5888

e-ISSN 2249-4618

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GJMR-K Classification NLMC Code: QY 4

Submission ReceivedApril 5, 2021
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Accepted April 30, 2021
Published May 15, 2021

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February 27, 2021

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