Artificial Intelligence (AI) in Pathology â€“ A Summary and Challenges

Archana Buch; Rohan Kulkarni

doi:10.34257/GJMRKVOL21IS2PG23

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

Archana Buch * §

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Rohan Kulkarni

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§ Dr. DY Patil Medical College Pune, Maharashtra

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

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MRG5237

DOI
10.34257/GJMRKVOL21IS2PG23

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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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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Archana Buch, Rohan Kulkarni. 2021. "Artificial Intelligence (AI) in Pathology – A Summary and Challenges". 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/gjmr

Print ISSN 0975-5888

e-ISSN 2249-4618

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GJMR-K Classification NLMC Code: QY 4

Submission ReceivedApril 5, 2021
Peer Review Double Blind
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Accepted April 30, 2021
Published May 15, 2021

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v1.2

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February 27, 2021

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English

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