大数据
转化式学习
计算机科学
人工智能应用
领域(数学)
生物信号
数据科学
人工智能
模式
生物医学技术
工程类
精密医学
医学影像学
医学研究
工程伦理学
数字健康
个性化医疗
个性化
作者
Peiran Song,Xuan Tang,Xukun Lv,Rui L. Reis,Xiao Chen,Long Bai,Jiacan Su
摘要
Abstract Artificial intelligence (AI) has emerged as a transformative force in biomedical engineering, catalyzing a shift toward data‐driven, intelligent research paradigms. With its advanced capabilities in computation, pattern recognition, and large‐scale data analysis, AI significantly enhances the efficiency, precision, and reproducibility of biomedical research. In particular, the convergence of AI with biomedical engineering has given rise to the emerging field of Digital Biomedical Engineering, which emphasizes the integration of AI technologies, big data analytics, and computational modeling to enable smart, predictive, and personalized solutions across life sciences. This review provides a comprehensive overview of the current landscape of AI applications in biomedical engineering, highlighting advances in medical image analysis, biosignal processing, biomaterials design, and drug development. It also emphasizes how AI improves diagnostic precision, accelerates material and drug discovery, and fosters personalized and predictive medicine. Additionally, it discusses the limitations and regulatory challenges of AI adoption, while outlining future directions to guide research innovation and clinical translation in the era of digital biomedicine.
科研通智能强力驱动
Strongly Powered by AbleSci AI