Evidence map›Paper›PMID 41637748›Full record

ArticleJMIR cancer2026

Patients' Attitudes and Beliefs Toward Artificial Intelligence Use in Cancer Care: Cross-Sectional Survey Study.

Marco Santos Teles, Karolina Bryl, Susan Chimonas, Atif Khan, Andrew S Epstein, Bobby Daly, Han Xiao, Jun J Mao

Abstract read
In one paragraph

Article in JMIR cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Marco Santos TelesIntegrative Medicine Service, Memorial Sloan Kettering Cancer Center, 205 East 64th Street, New York, NY, 10022, United States, 1 646 608 8552.ORCID 0009-0000-0919-7979
Karolina BrylIntegrative Medicine Service, Memorial Sloan Kettering Cancer Center, 205 East 64th Street, New York, NY, 10022, United States, 1 646 608 8552.ORCID 0000-0002-1804-367X
Susan ChimonasDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID 0000-0002-7742-5950
Atif KhanDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID 0000-0001-8640-8530
Andrew S EpsteinGastrointestinal Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID 0000-0003-1808-3070
Bobby DalyThoracic Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID 0000-0001-6573-7650
Han XiaoGenitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID 0000-0001-5292-5687
Jun J MaoIntegrative Medicine Service, Memorial Sloan Kettering Cancer Center, 205 East 64th Street, New York, NY, 10022, United States, 1 646 608 8552.ORCID 0000-0001-9229-0380

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
Research & Methods CoreP50CA271357 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI MICHAEL J MORRIS, KATHERINE S PANAGEAS · 2022 to 2026
$7.7M
NCI NIH HHS P30 CA008748NCI NIH HHS P50 CA271357
6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is being rapidly integrated into oncologic care, yet little is known about how patients perceive these applications. Understanding patient perceptions is critical to ensuring AI applications align with their needs and preferences. Objective: This study aimed to evaluate oncology patients' attitudes and beliefs on the use of AI across clinical touchpoints in cancer care. Methods: We conducted a cross-sectional survey study with adult oncology patients from September to December 2024. The survey assessed patients' comfort with AI use across 8 clinical touchpoints of cancer care (eg, screening, diagnosis, treatment) on a 5-point Likert scale (1=very uncomfortable to 5=very comfortable). Patients also rated their concerns about AI, including potential harms related to its use (eg, medical errors, privacy breaches), on a 3-point Likert scale (1=not concerned to 3=very concerned). Results: Of 383 patients approached, 330 (86.2% response rate) participated; 184 (55.9%) were male, 162 (49.4%) were aged 65 years or older, 35 (10.8%) were Black, 40 (12.1%) were Hispanic or Latino, and 233 (72.6%) were actively receiving cancer treatment. Patients were most comfortable with AI use in cancer screening (80.2%) and supportive care applications, including exercise (78.2%), diet (74.8%), and herbs/supplements (72.4%). Patients were least comfortable with AI use to assist with diagnosis (70.4%), symptom management (67.5%), treatment planning (64.8%), and prognosis (61.5%). Nonetheless, about half (49.7%) were at least somewhat concerned with the use of AI in cancer care, most commonly about the loss of human interaction and medical errors. Conclusions: Although the majority of oncology patients had a favorable view of AI in cancer care, nearly half had concerns about potential harms. Incorporating patient perspectives into AI development is essential for patient-centered and high-quality cancer care.

Indexed as

Artificial IntelligenceHealth Knowledge, Attitudes, PracticeNeoplasmsAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and Questionnairesartificial intelligencecancer caredigital healthpatient attitudespatient perspectives

Identifiers

PMID41637748
PMCPMC12872120

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.