Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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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.
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): .
Crossref Journal DOI 10.17406/gjmra
Print ISSN 0975-5888
e-ISSN 2249-4618
The methods for personal identification and authentication are no exception.
Total Score: 132
Country: United States
Subject: Global Journal of Medical Research - K: Interdisciplinary
Authors: Archana Buch, Rohan Kulkarni (PhD/Dr. count: 0)
View Count (all-time): 113
Total Views (Real + Logic): 2050
Total Downloads (simulated): 975
Publish Date: 2021 02, Sat
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Neural Networks and Rules-based Systems used to Find Rational and
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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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