Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.

Autor: Bryant AK; 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., Zamora-Resendiz R; Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA., Dai X; Computational Science Initiative, Brookhaven National Laboratory, Upton, New York, USA., Morrow D; Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA., Lin Y; Computational Science Initiative, Brookhaven National Laboratory, Upton, New York, USA., Jungles KM; Department of Pharmacology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA., Rae JM; Department of Pharmacology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.; Department of Internal Medicine, University of Michigan School of Medicine, Ann Arbor, Michigan, USA., Tate A; Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA., Pearson AN; Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA., Jiang R; Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.; Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA., Fritsche L; Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA., Lawrence TS; Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA., Zou W; Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, Michigan, USA.; Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.; Graduate Program in Immunology, University of Michigan, Ann Arbor, Michigan, USA., Schipper M; Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.; Department of Pharmacology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA., Ramnath N; Division of Hematology Oncology, Department of Medicine, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.; Division of Hematology Oncology, Department of Medicine, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, USA., Yoo S; Computational Science Initiative, Brookhaven National Laboratory, Upton, New York, USA., Crivelli S; Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA., Green MD; 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.; Graduate Program in Immunology, University of Michigan, Ann Arbor, Michigan, USA.; Graduate Program in Cancer Biology, University of Michigan, Ann Arbor, Michigan, USA.; Department of Microbiology and Immunology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
Jazyk: angličtina
Zdroj: Cancer medicine [Cancer Med] 2024 Jun; Vol. 13 (12), pp. e7253.
DOI: 10.1002/cam4.7253
Abstrakt: 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.
(© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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