Hope Andrew, Verduin Maikel, Dilling Thomas J, Choudhury Ananya, Fijten Rianne, Wee Leonard, Aerts Hugo Jwl, El Naqa Issam, Mitchell Ross, Vooijs Marc, Dekker Andre, de Ruysscher Dirk, Traverso Alberto
Department of Radiation Oncology, University of Toronto, Toronto, ON 5MT 1P5, Canada.
Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON 5MT 1P5, Canada.
Cancers (Basel). 2021 May 14;13(10):2382. doi: 10.3390/cancers13102382.
Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.
局部晚期非小细胞肺癌患者约占新诊断肺癌患者的三分之一。目前仍有很大的未满足需求,即需要找到能够提高这些患者生存率同时将治疗副作用降至最低的治疗策略。增加患者数据(影像、电子健康记录、患者报告结局和基因组学)的可获取性将有助于应用人工智能算法来改善治疗选择。在本综述中,我们讨论了人工智能(AI)如何成为改善临床决策支持系统不可或缺的一部分。要实现这一点,必须定义人工智能的路线图。我们定义了六个里程碑,涉及从医生到患者的广泛利益相关者,我们认为这些里程碑对于人工智能顺利过渡到临床是必要的。