Evidence map›Paper›PMID 39903075›Full record

ReviewRadiology2025

Foundation Models in Radiology: What, How, Why, and Why Not.

Magdalini Paschali, Zhihong Chen, Louis Blankemeier, Maya Varma, Alaa Youssef, Christian Bluethgen, Curtis Langlotz, Sergios Gatidis, Akshay Chaudhari

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 2 pooled it
–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

52 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Identifying the presence of disc herniations in lumbar spine MRI using Gemini 3.1 Pro.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  7. Review
  8. Article
  9. Article
  10. Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026
    Review
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
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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

9 authors.

Magdalini PaschaliFrom the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).ORCID 0000-0003-4967-3137
Zhihong ChenFrom the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).ORCID 0000-0003-4064-7987
Louis BlankemeierFrom the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).ORCID 0000-0001-6365-9767
Maya VarmaFrom the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).ORCID 0000-0003-0693-7753
Alaa YoussefFrom the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).ORCID 0000-0001-6505-8236
Christian BluethgenFrom the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).ORCID 0000-0001-7321-5676
Curtis LanglotzFrom the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).ORCID 0000-0002-8972-8051
Sergios GatidisFrom the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).ORCID 0000-0002-6928-4967
Akshay ChaudhariFrom the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).ORCID 0000-0002-3667-6796

Funding

MEDICAL IMAGING AND DATA RESOURCE CENTER (MIDRC) YEAR 375N92020D00021 · NIBIB · UNIVERSITY OF CHICAGO · PI GIGER, MARYELLEN · 2021 to 2025
$16.6M
NHLBI NIH HHS 75N92020D00021
6 · The paper itself

Abstract

Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models (FMs), are trained on extensive corpora of unlabeled data and demonstrate high performance across various tasks. FMs have recently received extensive attention from academic, industry, and regulatory bodies. Given the potentially transformative impact that FMs can have on the field of radiology, radiologists must be aware of potential pathways to train these radiology-specific FMs, including understanding both the benefits and challenges. Thus, this review aims to explain the fundamental concepts and terms of FMs in radiology, with a specific focus on the requirements of training data, model training paradigms, model capabilities, and evaluation strategies. Overall, the goal of this review is to unify technical advances and clinical needs for safe and responsible training of FMs in radiology to ultimately benefit patients, providers, and radiologists.

Indexed as

Artificial IntelligenceDeep LearningRadiologyHumans

Identifiers

PMID39903075
PMCPMC11868850

What OpenQuestion holds

Textmetadata
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.