{"title":"Artificial intelligence-powered biomedical imaging: Recent achievements and challenges","authors":"Ashkan Ebadi , Alexander Wong","doi":"10.1016/j.cobme.2026.100650","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, there have been remarkable advancements in artificial intelligence (AI) techniques, particularly in their application to biomedical imaging. This integration has opened up new possibilities for early and improved diagnosis, automation, and interoperability across various medical applications. This review explores the key developments in AI-driven biomedical imaging, examining the techniques and applications that have evolved. We highlight recent enhancements in various areas, such as early-stage diagnostics and explainability. Additionally, we address the challenges and limitations while shedding light on potential research directions to further integrate AI into clinical imaging, thereby enhancing patient-centered care. By synthesizing these key advancements and ongoing challenges, we aim to underscore AI's potential to transform biomedical imaging practices.</div></div>","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"38 ","pages":"Article 100650"},"PeriodicalIF":4.2000,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468451126000024","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/14 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, there have been remarkable advancements in artificial intelligence (AI) techniques, particularly in their application to biomedical imaging. This integration has opened up new possibilities for early and improved diagnosis, automation, and interoperability across various medical applications. This review explores the key developments in AI-driven biomedical imaging, examining the techniques and applications that have evolved. We highlight recent enhancements in various areas, such as early-stage diagnostics and explainability. Additionally, we address the challenges and limitations while shedding light on potential research directions to further integrate AI into clinical imaging, thereby enhancing patient-centered care. By synthesizing these key advancements and ongoing challenges, we aim to underscore AI's potential to transform biomedical imaging practices.