Evidence map›Paper›PMID 40062243›Full record

ArticlePeerJ. Computer science2025

Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review.

Daksh Dave, Adnan Akhunzada, Nikola Ivković, Sujan Gyawali, Korhan Cengiz, Adeel Ahmed, Ahmad Sami Al-Shamayleh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. 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.

Daksh DaveDepartment of Electrical Electronics, Birla Institute of Technology and Science, Birla Institute of Technology and Science, Pilani, India.
Adnan AkhunzadaCollege of Computing IT, University of Doha for Science Technology, Doha, Qatar.
Nikola IvkovićFaculty of Organization and Informatics, University of Zagreb, Pavlinska, Varazdin, Croatia.
Sujan GyawaliDepartment of Computer Science, Lamar University, Texas, United States of America.
Korhan CengizDepartment of Electrical-Electronics Engineering, Biruni University, Istanbul, Turkey.
Adeel AhmedDepartment of Information Technology, The University of Haripur, Haripur, Pakistan.
Ahmad Sami Al-ShamaylehFaculty of Information Technology, Department of Networks and Cybersecurity, Al-Ahliyya Amman University, Amman, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence into healthcare, particularly in mammography, holds immense potential for improving breast cancer diagnosis. Artificial intelligence (AI), with its ability to process vast amounts of data and detect intricate patterns, offers a solution to the limitations of traditional mammography, including missed diagnoses and false positives. This review focuses on the diagnostic accuracy of AI-assisted mammography, synthesizing findings from studies across different clinical settings and algorithms. The motivation for this research lies in addressing the need for enhanced diagnostic tools in breast cancer screening, where early detection can significantly impact patient outcomes. Although AI models have shown promising improvements in sensitivity and specificity, challenges such as algorithmic bias, interpretability, and the generalizability of models across diverse populations remain. The review concludes that while AI holds transformative potential in breast cancer screening, collaborative efforts between radiologists, AI developers, and policymakers are crucial for ensuring ethical, reliable, and inclusive integration into clinical practice.

Indexed as

Artificial intelligenceBreast cancerHealth careMammographyMedical imaging

Identifiers

PMID40062243
PMCPMC11888881

What OpenQuestion 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 OpenQuestion graph.