Artificial Intelligence (AI) in Pathology #x2013; A Summary and Challenges

Authors

  • Archana Buch

  • Rohan Kulkarni

DOI:

https://doi.org/10.34257/GJMRKVOL21IS2PG23

Keywords:

history of artificial intelligence (AI), AI in healthcare, deep learning (DL) in digital pathology (DP)

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 #x2018;physical slide on a physical microscope#x2019;. 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.

How to Cite

Archana Buch, & Rohan Kulkarni. (2021). Artificial Intelligence (AI) in Pathology #x2013; A Summary and Challenges. Global Journal of Medical Research, 21(K2), 23–34. https://doi.org/10.34257/GJMRKVOL21IS2PG23

Artificial Intelligence (AI) in Pathology #x2013; A Summary and Challenges

Published

2021-01-15