Artificial Intelligence in Contact Dermatitis: Current and Future Perspectives.

IF 3.2
Akriti Agrawal
{"title":"Artificial Intelligence in Contact Dermatitis: Current and Future Perspectives.","authors":"Akriti Agrawal","doi":"10.1177/17103568251376647","DOIUrl":null,"url":null,"abstract":"<p><p><u><b><i></i></b></u> Contact dermatitis (CD), which includes both allergic CD and irritant CD, is a common inflammatory condition that can pose significant diagnostic challenges. Although patch testing is the gold standard for identifying causative allergens for allergic contact dermatitis (ACD), it is time-consuming, subjective, and requires expert interpretation. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning, have shown promise in improving the accuracy, efficiency, and accessibility of CD diagnosis and management. This review explores current applications of AI in CD, drawing from 12 original studies that investigated AI-based image analysis, biomarker discovery, and patient risk profiling. Convolutional neural networks demonstrated high diagnostic accuracy (up to 99.5%) in interpreting patch test images, while ML algorithms successfully identified transcriptomic signatures distinguishing allergic CD from irritant CD. In addition, AI has been used to predict positive patch test outcomes and identify high-risk patients based on clinical and occupational factors. Despite these promising developments, limitations such as dataset bias, lack of standardization, and model interpretability remain. Nevertheless, AI represents a transformative tool in dermatology, offering the potential for standardized diagnostics, personalized care, and enhanced accessibility.</p>","PeriodicalId":93974,"journal":{"name":"Dermatitis : contact, atopic, occupational, drug","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dermatitis : contact, atopic, occupational, drug","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/17103568251376647","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Contact dermatitis (CD), which includes both allergic CD and irritant CD, is a common inflammatory condition that can pose significant diagnostic challenges. Although patch testing is the gold standard for identifying causative allergens for allergic contact dermatitis (ACD), it is time-consuming, subjective, and requires expert interpretation. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning, have shown promise in improving the accuracy, efficiency, and accessibility of CD diagnosis and management. This review explores current applications of AI in CD, drawing from 12 original studies that investigated AI-based image analysis, biomarker discovery, and patient risk profiling. Convolutional neural networks demonstrated high diagnostic accuracy (up to 99.5%) in interpreting patch test images, while ML algorithms successfully identified transcriptomic signatures distinguishing allergic CD from irritant CD. In addition, AI has been used to predict positive patch test outcomes and identify high-risk patients based on clinical and occupational factors. Despite these promising developments, limitations such as dataset bias, lack of standardization, and model interpretability remain. Nevertheless, AI represents a transformative tool in dermatology, offering the potential for standardized diagnostics, personalized care, and enhanced accessibility.

人工智能在接触性皮炎中的应用:当前和未来展望。
接触性皮炎(CD)包括过敏性皮炎和刺激性皮炎,是一种常见的炎症性疾病,可以带来重大的诊断挑战。虽然斑贴试验是鉴别过敏性接触性皮炎(ACD)致病性过敏原的金标准,但它耗时、主观,需要专家解释。人工智能(AI)的最新进展,特别是机器学习(ML)和深度学习,在提高CD诊断和管理的准确性、效率和可及性方面显示出了希望。本文回顾了目前人工智能在CD中的应用,引用了12项原始研究,这些研究调查了基于人工智能的图像分析、生物标志物发现和患者风险分析。卷积神经网络在解释贴片测试图像方面表现出很高的诊断准确性(高达99.5%),而ML算法成功识别出区分过敏性CD和过敏性CD的转录组特征。此外,人工智能已被用于预测贴片测试阳性结果,并根据临床和职业因素识别高风险患者。尽管有这些有希望的发展,但数据集偏差、缺乏标准化和模型可解释性等限制仍然存在。然而,人工智能代表了皮肤科的一种变革性工具,为标准化诊断、个性化护理和提高可及性提供了潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信