Authors:	Jartarkar, Shishira R. Cockerell, Clay J. Patil, Anant Kassir, Martin Babaei, Mahsa Weidenthaler-Barth, Beate Grabbe, Stephan Goldust, Mohamad
Title:	Artificial intelligence in Dermatopathology
Online publication date:	7-Mar-2024
Year of first publication:	2022
Language:	english
Abstract:	Introduction: Ever evolving research in medical field has reached an exciting stage with advent of newer technologies. With the introduction of digital microscopy, pathology has transitioned to become more digitally oriented speciality. The potential of artificial intelligence (AI) in dermatopathology is to aid the diagnosis, and it requires dermatopathologists' guidance for efficient functioning of artificial intelligence. Method: Comprehensive literature search was performed using electronic online databases “PubMed” and “Google Scholar.” Articles published in English language were considered for the review. Results: Convolutional neural network, a type of deep neural network, is considered as an ideal tool in image recognition, processing, classification, and segmentation. Implementation of AI in tumor pathology is involved in the diagnosis, grading, staging, and prognostic prediction as well as in identification of genetic or pathological features. In this review, we attempt to discuss the use of AI in dermatopathology, the attitude of patients and clinicians, its challenges, limitation, and potential opportunities in future implementation.
DDC:	610 Medizin 610 Medical sciences
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 04 Medizin
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-10165
Version:	Published version
Publication type:	Zeitschriftenaufsatz
Document type specification:	Review
License:	CC BY
Information on rights of use:	https://creativecommons.org/licenses/by/4.0/
Journal:	Journal of cosmetic dermatology 22 4
Pages or article number:	1163 1167
Publisher:	Wiley
Publisher place:	Oxford
Issue date:	2022
ISSN:	1473-2165
Publisher DOI:	10.1111/jocd.15565
Appears in collections:	DFG-491381577-H