Evidence map›Paper›PMID 42509379›Full record

ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2026

Hematology-Oncology Fellows' Use of Artificial Intelligence: A Multicenter Educational Practice and Needs Assessment Survey.

Ariela L Marshall, Richard C Godby, Marc Braunstein, Soo Young Kim, Scott Moerdler, Layla Van Doren, Juan Jose Chango Azanza, Ronak Mistry

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Article in Journal of cancer education : the official journal of the American Association for Cancer Education, 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
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0citing papers in PubMed
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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

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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

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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

8 authors.

Ariela L MarshallDepartment of Medicine, Division of Hematology, Oncology, and Transplantation, University of Minnesota, 420 Delaware Street SE, MMC 480, Minneapolis, MN, 55455, USA. mars2207@umn.edu.ORCID http://orcid.org/0000-0001-7388-0422
Richard C GodbyDivision of Hematology, Mayo Clinic, Rochester, MN, USA.
Marc BraunsteinNew York University Grossman Long Island School of Medicine, Mineola, NY, USA.
Soo Young KimNew York University, New York, NY, USA.
Scott MoerdlerDivision of Pediatric Hematology-Oncology, Rutgers Cancer Institute, New Brunswick, NJ, USA.
Layla Van DorenYale University, New Haven, Connecticut, USA.
Juan Jose Chango AzanzaMinneapolis Veterans Affairs Medical Center, Minneapolis, MN, USA.
Ronak MistryUniversity of Pennsylvania, Philadelphia, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) is rapidly being incorporated within healthcare, including medical education. Hematology-oncology (HO) is a complex field and AI is of great value in education and practice. We explored HO fellow's confidence and concerns with use of AI, AI training that HO fellows currently receive, and interest in future AI training. Multi-institutional survey administered to fellows in six adult HO programs. The survey was distributed electronically over one month with weekly reminders. Results were collected in RedCap and analyzed using R. 36 of 153 potential participants responded (23.5% response rate). Almost all (35, 97%) reported using AI. Fellows who reported using AI for work used it primarily for patient care (28, 82%) and/or research (29, 85%). Most were "somewhat confident" in the accuracy of AI-generated clinical information (25, 74%) and found AI "somewhat reliable" for clinical decision support (25, 74%). Respondents cited concerns about the use of AI in clinical practice, most often reliability of clinical data (32, 94%). Only one respondent (2.8%) received formal education on AI during fellowship and over who received informal training only reported its quality as "fair" or "poor" with 29 of these remaining 35 (83%) respondents interested in formal training. 31 fellows (86%) reported thinking AI would be important in their future career plans and 17 (49%) felt AI training should be required for HO fellows. While almost all HO fellows use AI, formal education in AI is rare in HO fellowship. Most fellows expressed interested in such training and believe that AI will be important in their careers, but also expressed concerns about use of AI, especially reliability of AI-generated clinical data. We plan to create formal curricula specific to HO training.

Indexed as

Artificial IntelligenceHematologyMedical EducationOncology

Identifiers

What OpenQuestion holds

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Registered trials

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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.