Evidence map›Paper›PMID 42202236›Full record

ArticleJCO clinical cancer informatics2026

Knowledge and Use of Artificial Intelligence Among Oncology Faculty and Trainees at a Comprehensive Cancer Center in 2025.

Keri Schadler, Ann Klopp, Ramez Kouzy, Pavitra P Krishnamani, Rebekah Filson, Aiden Moody, Elizabeth Grubbs, Nicole Munoz, Lauren Payne, Shawn Stapleton and 1 more

Abstract read
In one paragraph

Article in JCO clinical cancer informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Keri SchadlerDepartment of Pediatrics Research, The University of Texas MD Anderson Cancer Center, Houston, TX.
Ann KloppDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.ORCID 0000-0002-2081-0735
Ramez KouzyDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.ORCID 0000-0002-1521-0495
Pavitra P KrishnamaniDepartment of Emergency Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.ORCID 0000-0002-3589-1902
Rebekah FilsonDepartment of Academic Affairs, The University of Texas MD Anderson Cancer Center, Houston, TX.ORCID 0009-0001-9849-5851
Aiden MoodyDepartment of Pediatrics Research, The University of Texas MD Anderson Cancer Center, Houston, TX.
Elizabeth GrubbsDepartment of Emergency Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.ORCID 0000-0002-1550-8427
Nicole MunozNew York University, New York, NY.
Lauren PayneInstitute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Shawn StapletonInstitute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Caroline ChungDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.ORCID 0000-0002-9662-1519

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeArtificial intelligence (AI) has been used in medicine for decades, but recent advances in machine learning and large language models have rapidly expanded its accessibility and applications in oncology. Although AI offers efficiency in data analysis, synthesis, and communication, important questions remain regarding output reliability, research rigor, data security, and appropriate reliance on automated tools, underscoring the need for thoughtful implementation and training.

methodsTo better understand current AI use in academic oncology and perceptions about utility, we conducted a survey of faculty and trainees at a comprehensive cancer center.

resultsAmong 227 respondents (55% women; 64% clinical faculty), 58% reported using AI several times per month or more, whereas 15% had never used it. The most common applications were summarizing academic or research information and generating data visualizations. Attitudes toward AI were generally positive: 74% agreed that AI will improve cancer diagnosis within the next decade. In contrast, views were more cautious regarding end-of-life decision making, with 35% disagreeing that AI would be beneficial in that context. Despite broad interest, a substantial training gap emerged. Nearly 93% of respondents endorsed the need for dedicated AI training, and approximately half reported not knowing where to find reliable learning resources. Lower AI use was associated with female gender and age over 60 years.

conclusionAI use among oncology faculty and trainees is common but variable, with differences across demographic and professional groups. These findings highlight the need for structured, accessible training and institutional guidance to promote appropriate, equitable, and high-quality integration of AI into oncology research and clinical care.

Indexed as

Artificial IntelligenceFaculty, MedicalMedical OncologyAdultFemaleHumansMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID42202236
PMCPMC13225107

What OpenQuestion holds

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LicenceCC BY
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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.