Evidence map›Paper›PMID 38899720›Full record

ReviewCancer medicine2024

Artificial intelligence to unlock real-world 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 and 8 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. When evidence meets artificial intelligence.Lancet regional health. Americas · 2026
    Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Oncology's ongoing struggle with evaluating quality of life limiting, treatment-related toxicity.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025
    Article
  9. Review
  10. Review
  11. Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Alex K BryantDepartment of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.ORCID 0000-0003-0194-8381
Rafael Zamora-ResendizApplied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Xin DaiComputational Science Initiative, Brookhaven National Laboratory, Upton, New York, USA.
Destinee MorrowApplied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Yuewei LinComputational Science Initiative, Brookhaven National Laboratory, Upton, New York, USA.
Kassidy M JunglesDepartment of Pharmacology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
James M RaeDepartment of Pharmacology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
Akshay TateDepartment of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
Ashley N PearsonDepartment of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
Ralph JiangDepartment of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
Lars FritscheDepartment of Statistics, University of Michigan, Ann Arbor, Michigan, USA.
Theodore S LawrenceDepartment of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
Weiping ZouDepartment of Statistics, University of Michigan, Ann Arbor, Michigan, USA.
Matthew SchipperDepartment of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
Nithya RamnathDivision of Hematology Oncology, Department of Medicine, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
Shinjae YooComputational Science Initiative, Brookhaven National Laboratory, Upton, New York, USA.
Silvia CrivelliApplied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Michael D GreenDepartment of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.ORCID 0000-0003-4951-7118

Funding

Ablating Liver Metastases with SBRT to Enhance Immune Checkpoint Blockade in MelanomaR01CA276217 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI THEODORE S LAWRENCE · 2023 to 2026
$2.2M
BLRD VA I01 BX005267LUNGevity Foundation 2021-07Melanoma Research Alliance 689853NCI NIH HHS R01 CA276217NCI NIH HHS R01CA276217Veterans Affairs 150CU000182Veterans Affairs BX005267Veterans Affairs MVP064Veterans Affairs VA 150CU000182
6 · The paper itself

Abstract

purposeReal 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.

methodsWe performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology.

resultsArtificial 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.

conclusionsDespite 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.

Indexed as

Artificial IntelligenceMedical OncologyNeoplasmsHumansArtificial IntelligenceCancer Outcomes ResearchLarge language modelsObservational Dataprognostic factor

Identifiers

PMID38899720
PMCPMC11187737

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.