Artificial intelligence to unlock <scp>real‐world</scp> evidence in clinical oncology: A primer on recent advances
Alex K. Bryant, Rafael Zamora‐Resendiz, Xin Dai, Destinee Morrow, Yuewei Lin, Kassidy M. Jungles, James M. Rae, Akshay Tate, Ashley N. Pearson, Ralph Jiang, Lars G. Fritsche, Theodore S. Lawrence, Weiping Zou, Matthew J. Schipper, Nithya Ramnath, Shinjae Yoo, Silvia Crivelli, Michael D. Green
Abstract
PURPOSE: 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. METHODS: We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. RESULTS: 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. CONCLUSIONS: 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.