Evidence map›Paper›PMID 35280945›Full record

ReviewPolish journal of radiology2022

Applications and challenges of artificial intelligence in diagnostic and interventional radiology.

Joseph Waller, Aisling O'Connor, Eleeza Rafaat, Ahmad Amireh, John Dempsey, Clarissa Martin, Muhammad Umair

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Polish journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 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

36 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Review
  11. Radiological data processing system: lifecycle management and annotation.International journal of computer assisted radiology and surgery · 2025
    Article
  12. Application of Machine Learning to Breast MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
    Review
  13. Review
  14. IR-GPT: AI Foundation Models to Optimize Interventional Radiology.Cardiovascular and interventional radiology · 2025
    Review
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. 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

7 authors.

Joseph WallerDrexel University College of Medicine, USA.
Aisling O'ConnorUniversity of Washington, Seattle, WA 98195, USA.
Eleeza RafaatLoyola University Chicago Stritch School of Medicine, Maywood, IL 60153, USA.
Ahmad AmirehDuke University, Department of Biology, Durham, NC 27708, USA.
John DempseyBoston College, Chestnut Hill, MA 02467, USA.
Clarissa MartinUniversity of Pennsylvania, Philadelphia PA 17101, USA.
Muhammad UmairNorthwestern University Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Machine learning (ML) and deep learning (DL) can be utilized in radiology to help diagnosis and for predicting management and outcomes based on certain image findings. DL utilizes convolutional neural networks (CNN) and may be used to classify imaging features. The objective of this literature review is to summarize recent publications highlighting the key ways in which ML and DL may be applied in radiology, along with solutions to the problems that this implementation may face. Material and methods: Twenty-one publications were selected from the primary literature through a PubMed search. The articles included in our review studied a range of applications of artificial intelligence in radiology. Results: The implementation of artificial intelligence in diagnostic and interventional radiology may improve image analysis, aid in diagnosis, as well as suggest appropriate interventions, clinical predictive modelling, and trainee education. Potential challenges include ethical concerns and the need for appropriate datasets with accurate labels and large sample sizes to train from. Additionally, the training data should be representative of the population to which the future ML platform will be applicable. Finally, machines do not disclose a statistical rationale when expounding on the task purpose, making them difficult to apply in medical imaging. Conclusions: As radiologists report increased workload, utilization of artificial intelligence may provide improved outcomes in medical imaging by assisting, rather than guiding or replacing, radiologists. Further research should be done on the risks of AI implementation and how to most accurately validate the results.

Indexed as

artificial intelligenceimagingmachine learningradiology

Identifiers

PMID35280945
PMCPMC8906183

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.