医学
无线电技术
医学物理学
放射基因组学
人工智能应用
精密医学
医学影像学
人工智能
医疗保健
放射科
病理
计算机科学
经济增长
经济
作者
Simone Vicini,Chandra Bortolotto,Marco Rengo,Daniela Ballerini,Davide Bellini,Iacopo Carbone,Lorenzo Preda,Andrea Laghi,Francesca Coppola,Lorenzo Faggioni
标识
DOI:10.1007/s11547-022-01512-6
摘要
The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response.
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