电流(流体)
医学物理学
医学
数据科学
计算机科学
工程类
电气工程
作者
John Kang,Kyle J. Lafata,Ellen Kim,Christopher M. K. L. Yao,Frank Lin,Tim Rattay,Harsha Nori,Evangelia Katsoulakis,Christoph I. Lee
标识
DOI:10.1136/bmjonc-2023-000134
摘要
Oncology is becoming increasingly personalised through advancements in precision in diagnostics and therapeutics, with more and more data available on both ends to create individualised plans. The depth and breadth of data are outpacing our natural ability to interpret it. Artificial intelligence (AI) provides a solution to ingest and digest this data deluge to improve detection, prediction and skill development. In this review, we provide multidisciplinary perspectives on oncology applications touched by AI—imaging, pathology, patient triage, radiotherapy, genomics-driven therapy and surgery—and integration with existing tools—natural language processing, digital twins and clinical informatics.
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