Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, USA.
Cancer Med. 2024 Jun;13(12):e7253. doi: 10.1002/cam4.7253.
Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology.
We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology.
Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping.
Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.
真实世界证据对于理解新的肿瘤治疗方法的应用、监测癌症结果和发现意外毒性至关重要。在实践中，真实世界证据的收集往往既困难又耗时，需要昂贵且耗时的人工病例发现和临床文本注释。在这篇综述中，我们总结了人工智能在肿瘤学中收集和分析真实世界证据的最新进展。
我们对人工智能在肿瘤学中的主要应用趋势和最新文献进行了叙述性综述。
人工智能（AI）方法正越来越多地被用于大规模高效地对患者和肿瘤进行表型分析。这些工具还可以通过整合影像学、病理学和基因组数据集进行多模态分析，提供新的生物学见解并改善风险预测。定制语言处理管道和大型语言模型在临床预测和表型分析方面具有广阔的应用前景。
尽管取得了快速进展，但仍需要在计算、泛化能力、可解释性和可靠性方面取得进一步进展，并进行前瞻性验证，以便将 AI 方法整合到常规临床护理和新型治疗方法的实时监测中。
