Evidence map›Paper›PMID 41030223›Full record

ArticleMedical physics2025

Teaching AI for Radiology Applications: A Multisociety‑Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM.

Felipe Kitamura, Timothy Kline, Daniel Warren, Linda Moy, Roxana Daneshjou, Farhad Maleki, Igor Santos, Judy Gichoya, Walter Wiggins, Brian Bialecki and 21 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2025. 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. 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

31 authors.

Felipe KitamuraSociety of Imaging Informatics in Medicine, Leesburg, USA.
Timothy KlineSociety of Imaging Informatics in Medicine, Leesburg, USA.
Daniel WarrenCarle College of Medicine, University of Illinois Urbana-Champaign, Urbana, USA.
Linda MoyNew York University Grossman School of Medicine, New York, New York, USA.
Roxana DaneshjouDepartment of Biomedical Data Science and Dermatology, Stanford School of Medicine, Stanford, California, USA.
Farhad MalekiDepartment of Computer Science, University of Calgary, Calgary, Alberta, Canada.
Igor SantosIonic Health, Porto, Portugal.
Judy GichoyaEmory University, Atlanta, Georgia, USA.
Walter WigginsGreensboro Radiology, Durham, North Carolina, USA.
Brian BialeckiAmerican College of Radiology, Reston, Virginia, USA.
Kevin O'DonnellCanon Medical Research USA, Vernon Hills, USA.
Adam E FlandersThomas Jefferson University, Philadelphia, Pennsylvania, USA.
Matt MorganUniversity of Utah, Salt Lake City, Utah, USA.
Nabile SafdarSociety of Imaging Informatics in Medicine, Leesburg, USA.
Katherine P AndrioleBrigham and Women's Hospital, Harvard Medical School, Mass General Brigham AI, Boston, Massachusetts, USA.
Raym GeisNational Jewish Health, Denver, Colorado, USA.
Bibb AllenBirmingham Radiological Group, Birmingham, Alabama, USA.
Keith DreyerAmerican College of Radiology, Data Science Institute, Reston, Virginia, USA.
Matt LungrenDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.
Monica J WoodMount Auburn Hospital, Harvard Medical School, Cambridge, Massachusetts, USA.
Marc KohliDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.
Steve LangerSociety of Imaging Informatics in Medicine, Leesburg, USA.
George ShihWeill Cornell Medicine, New York, New York, USA.
Eduardo FarinaSociety of Imaging Informatics in Medicine, Leesburg, USA.
Charles E KahnUniversity of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ingrid ReiserUniversity of Chicago, Chicago, Illinois, USA.
Maryellen GigerUniversity of Chicago, Chicago, Illinois, USA.
Christoph WaldDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
John MonganDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.
Tessa CookUniversity of Pennsylvania, Philadelphia, Pennsylvania, USA.
Neil TenenholtzSociety of Imaging Informatics in Medicine, Leesburg, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical imaging is undergoing a transformation driven by the advent of new, highly effective, machine learning techniques paired with increases in computational capabilities (Cheng et al. 2021; Gilson et al. 2023; Almeida et al. 2024; Krishna et al. 2024). These advanced algorithms have the potential to improve disease detection, diagnosis, prognosis, and treatment outcomes. However, the complexity of machine learning models, the large amounts of curated and annotated data required by some methods, and the potential for bias and error make it challenging for individuals to safely and effectively leverage these methods (Lin et al. 2024; Guo et al. 2024; Xu et al. 2024; Linguraru et al. 2024; Wood et al. 2019). To address these challenges, the American Association of Physicists in Medicine (AAPM), American College of Radiology (ACR), Radiological Society of North America (RSNA), and Society for Imaging Informatics in Medicine (SIIM) have worked together to develop a syllabus detailing a recommended set of competencies for medical imaging professionals interacting with these systems. This guide is aimed at four different personas: users of AI systems, purchasers of AI systems, individuals who provide clinical expertise during the development of AI systems ("clinical collaborators"), and developers of AI systems.1 This is a syllabus, not a curriculum, and is intentional in this scope. Recognizing that individuals may benefit from different presentations of the same material, this work enumerates a series of relevant competencies but does not prescribe, nor offer, a method of instruction (Schuur, Rezazade Mehrizi, and Ranschaert 2021; Garin et al. 2023). By addressing the task-specific demands of each role, this guide will enable medical imaging professionals to utilize machine learning systems more safely and effectively, ultimately improving patient care and outcomes.

Indexed as

Artificial IntelligenceCurriculumRadiologyTeachingHumansSocieties, MedicalAI educationAI syllabuseducationradiologyteaching

Identifiers

PMID41030223
PMCPMC12485864

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.