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Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends

Background: Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique n...

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Autores principales: Li, Zhouxiao, Koban, Konstantin Christoph, Schenck, Thilo Ludwig, Giunta, Riccardo Enzo, Li, Qingfeng, Sun, Yangbai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693628/
https://www.ncbi.nlm.nih.gov/pubmed/36431301
http://dx.doi.org/10.3390/jcm11226826
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author Li, Zhouxiao
Koban, Konstantin Christoph
Schenck, Thilo Ludwig
Giunta, Riccardo Enzo
Li, Qingfeng
Sun, Yangbai
author_facet Li, Zhouxiao
Koban, Konstantin Christoph
Schenck, Thilo Ludwig
Giunta, Riccardo Enzo
Li, Qingfeng
Sun, Yangbai
author_sort Li, Zhouxiao
collection PubMed
description Background: Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors. The aim of the study: For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.
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spelling pubmed-96936282022-11-26 Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends Li, Zhouxiao Koban, Konstantin Christoph Schenck, Thilo Ludwig Giunta, Riccardo Enzo Li, Qingfeng Sun, Yangbai J Clin Med Review Background: Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors. The aim of the study: For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly. MDPI 2022-11-18 /pmc/articles/PMC9693628/ /pubmed/36431301 http://dx.doi.org/10.3390/jcm11226826 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Li, Zhouxiao
Koban, Konstantin Christoph
Schenck, Thilo Ludwig
Giunta, Riccardo Enzo
Li, Qingfeng
Sun, Yangbai
Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
title Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
title_full Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
title_fullStr Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
title_full_unstemmed Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
title_short Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
title_sort artificial intelligence in dermatology image analysis: current developments and future trends
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693628/
https://www.ncbi.nlm.nih.gov/pubmed/36431301
http://dx.doi.org/10.3390/jcm11226826
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