Matineh Behzadi, Anahita Azinfar, Hawraa Ibrahim Alshakarchi, Yeganeh Khazaei, Ibrahim Saeed Gataa, Gordon A Ferns, Hamid Naderi, Amir Avan, Hamid Fiuji, Masoud Pezeshki Rad
{"title":"The Potential Diagnostic Application of Artificial Intelligence in Breast Cancer.","authors":"Matineh Behzadi, Anahita Azinfar, Hawraa Ibrahim Alshakarchi, Yeganeh Khazaei, Ibrahim Saeed Gataa, Gordon A Ferns, Hamid Naderi, Amir Avan, Hamid Fiuji, Masoud Pezeshki Rad","doi":"10.2174/0113816128369168250311172823","DOIUrl":null,"url":null,"abstract":"<p><p>Breast cancer poses a significant global health challenge, necessitating improved diagnostic and treatment strategies. This review explores the role of artificial intelligence (AI) in enhancing breast cancer pathology, emphasizing risk assessment, early detection, and analysis of histopathological and mammographic data. AI platforms show promise in predicting breast cancer risks and identifying tumors up to three years before clinical diagnosis. Deep learning techniques, particularly convolutional neural networks (CNNs), effectively classify cancer subtypes and grade tumor risk, achieving accuracy comparable to expert radiologists. Despite these advancements, challenges, such as the need for high-quality datasets and integration into clinical workflows, persist. Continued research on AI technologies is essential for advancing breast cancer detection and improving patient outcomes.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current pharmaceutical design","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0113816128369168250311172823","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Breast cancer poses a significant global health challenge, necessitating improved diagnostic and treatment strategies. This review explores the role of artificial intelligence (AI) in enhancing breast cancer pathology, emphasizing risk assessment, early detection, and analysis of histopathological and mammographic data. AI platforms show promise in predicting breast cancer risks and identifying tumors up to three years before clinical diagnosis. Deep learning techniques, particularly convolutional neural networks (CNNs), effectively classify cancer subtypes and grade tumor risk, achieving accuracy comparable to expert radiologists. Despite these advancements, challenges, such as the need for high-quality datasets and integration into clinical workflows, persist. Continued research on AI technologies is essential for advancing breast cancer detection and improving patient outcomes.
期刊介绍：
Current Pharmaceutical Design publishes timely in-depth reviews and research articles from leading pharmaceutical researchers in the field, covering all aspects of current research in rational drug design. Each issue is devoted to a single major therapeutic area guest edited by an acknowledged authority in the field.
Each thematic issue of Current Pharmaceutical Design covers all subject areas of major importance to modern drug design including: medicinal chemistry, pharmacology, drug targets and disease mechanism.
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