Evidence map›Paper›PMID 41967073›Full record

ArticleThe oncologist2026

OncoEducate: a pilot study of generative AI to enhance patient-clinician communication in genitourinary cancer care.

Henry K Litt, Amelia Wodzinski, Pearl Subramanian, Mackenzie Donovan, Neha Vapiwala, Vivek Narayan, Lin Mei, Samuel Takvorian, Naomi B Haas, Ravi B Parikh and 2 more

Abstract read
In one paragraph

Article in The oncologist, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

12 authors.

Henry K LittDivision of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.ORCID 0000-0002-3158-8797
Amelia WodzinskiAbramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Pearl SubramanianDepartment of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Mackenzie DonovanAbramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Neha VapiwalaAbramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Vivek NarayanDivision of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Lin MeiDivision of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Samuel TakvorianDivision of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Naomi B HaasDivision of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Ravi B ParikhDepartment of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA, 30322, United States.
Ronak MistryDivision of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Ronac MamtaniDivision of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.ORCID 0000-0002-8267-5356

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

OncoEducate is a clinician-supervised generative artificial intelligence (AI) application designed to deliver standardized, regimen-level, plain-language education at the point of care. We developed a templated handout generator using a locked prompt and predefined content structure that incorporates diagnosis, treatment regimen, and treatment intent, with clinician review prior to patient distribution. We then conducted a prospective, two-phase pilot at a single academic cancer center. In Phase I, six clinicians evaluated AI-generated handouts for nine commonly used palliative-intent genitourinary (GU) oncology regimens using 7-point Likert-scale items. In Phase II, patients with advanced kidney, prostate, or urothelial cancers initiating palliative-intent therapy received clinician-reviewed handouts and completed follow-up surveys at the next office or infusion visit or by telephone when in-person completion was not feasible. Clinicians rated handouts as useful (median 6/7) and accessible (median 6.5/7), with median accuracy ratings of 6-7/7 across domains. During an 8-week period, 20 of 21 approached patients enrolled (95%). Patients rated handouts as informative and readable (median 7/7 for both), and 65% correctly identified treatment intent as palliative. These findings support the feasibility and acceptability of clinician-reviewed, templated AI-generated handouts in routine GU oncology care, justifying larger randomized studies to evaluate clinical impact.

Indexed as

Physician-Patient RelationsUrogenital NeoplasmsAgedArtificial IntelligenceCommunicationFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedPilot ProjectsProspective Studiesartificial intelligencegenitourinary oncologypatient educationtreatment intent

Identifiers

PMID41967073
PMCPMC13102169

What Socratic 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 Socratic graph.