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

Bryant, Alex K.; Zamora‐Resendiz, Rafael; Dai, Xin; Morrow, Destinee; Lin, Yuewei; Jungles, Kassidy M.; Rae, James M.; Tate, Akshay; Pearson, Ashley N.; Jiang, Ralph; Fritsche, Lars; Lawrence, Theodore S.; Zou, Weiping; Schipper, Matthew; Ramnath, Nithya; Yoo, Shinjae; Crivelli, Silvia; Green, Michael D.

doi:10.1002/cam4.7253

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

Journal Article · Thu Jun 20 00:00:00 EDT 2024 · Cancer Medicine

DOI:https://doi.org/10.1002/cam4.7253· OSTI ID:2396887

^[1]; Zamora‐Resendiz, Rafael ^[2]; Dai, Xin ^[3]; Morrow, Destinee ^[2]; Lin, Yuewei ^[3]; Jungles, Kassidy M. ^[4]; Rae, James M. ^[4]; Tate, Akshay ^[4]; Pearson, Ashley N. ^[4]; Jiang, Ralph ^[5]; Fritsche, Lars ^[6]; Lawrence, Theodore S. ^[4]; Zou, Weiping ^[6]; Schipper, Matthew ^[4]; Ramnath, Nithya ^[1]; Yoo, Shinjae ^[3]; Crivelli, Silvia ^[2]; ^[7]

University of Michigan Medical School, Ann Arbor, MI (United States); Veterans Affairs (VA) Ann Arbor Health Care, MI (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Brookhaven National Laboratory (BNL), Upton, NY (United States)
University of Michigan Medical School, Ann Arbor, MI (United States)
University of Michigan Medical School, Ann Arbor, MI (United States); University of Michigan, Ann Arbor, MI (United States)
University of Michigan, Ann Arbor, MI (United States)
University of Michigan Medical School, Ann Arbor, MI (United States); Veterans Affairs (VA) Ann Arbor Health Care, MI (United States); University of Michigan, Ann Arbor, MI (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2396887

Journal Information:: Cancer Medicine, Journal Name: Cancer Medicine Journal Issue: 12 Vol. 13; ISSN 2045-7634

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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