Artificial intelligence across oncology specialties: current applications and emerging tools

John Kang, Kyle Lafata, Ellen Kim, Christopher Yao, Frank Lin, Tim Rattay, Harsha Nori, Evangelia Katsoulakis, Christoph Ilsuk Lee
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引用次数: 0

Abstract

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.
肿瘤专业的人工智能:当前应用和新兴工具
通过在诊断和治疗的精确性方面取得的进步,肿瘤学正变得越来越个性化,越来越多的两端数据可用于制定个性化计划。数据的深度和广度正在超越我们解读数据的自然能力。人工智能(AI)提供了一种解决方案,可用于摄取和消化这些数据洪流,从而改进检测、预测和技能开发。在这篇综述中,我们从多学科角度探讨了人工智能所涉及的肿瘤学应用--影像学、病理学、病人分流、放射治疗、基因组学驱动的治疗和手术--以及与现有工具的整合--自然语言处理、数字双胞胎和临床信息学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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