Comparative analysis of diagnostic performance in mammography: A reader study on the impact of AI assistance.

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science

Mammography*/methods ; Artificial Intelligence* ; Breast Neoplasms*/diagnostic imaging; Humans ; Female ; Retrospective Studies ; Middle Aged ; Adult ; Radiologists ; Aged ; ROC Curve ; Sensitivity and Specificity ; Malaysia ; Observer Variation

Competing Interests: The authors have declared that no competing interests exist.
Purpose: This study evaluates the impact of artificial intelligence (AI) assistance on the diagnostic performance of radiologists with varying levels of experience in interpreting mammograms in a Malaysian tertiary referral center, particularly in women with dense breasts.
Methods: A retrospective study including 434 digital mammograms interpreted by two general radiologists (12 and 6 years of experience) and two trainees (2 years of experience). Diagnostic performance was assessed with and without AI assistance (Lunit INSIGHT MMG), using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Inter-reader agreement was measured using kappa statistics.
Results: AI assistance significantly improved the diagnostic performance of all reader groups across all metrics (p < 0.05). The senior radiologist consistently achieved the highest sensitivity (86.5% without AI, 88.0% with AI) and specificity (60.5% without AI, 59.2% with AI). The junior radiologist demonstrated the highest PPV (56.9% without AI, 74.6% with AI) and NPV (90.3% without AI, 92.2% with AI). The trainees showed the lowest performance, but AI significantly enhanced their accuracy. AI assistance was particularly beneficial in interpreting mammograms of women with dense breasts.
Conclusion: AI assistance significantly enhances the diagnostic accuracy and consistency of radiologists in mammogram interpretation, with notable benefits for less experienced readers. These findings support the integration of AI into clinical practice, particularly in resource-limited settings where access to specialized breast radiologists is constrained.
(Copyright: © 2025 Ramli Hamid et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Technol Cancer Res Treat. 2022 Jan-Dec;21:15330338221075172. (PMID: 35060413)
Radiol Artif Intell. 2019 Jul 31;1(4):e180096. (PMID: 32076660)
Radiography (Lond). 2023 Mar;29(2):340-346. (PMID: 36731351)
Breast Cancer Res. 2024 Apr 22;26(1):68. (PMID: 38649889)
Healthcare (Basel). 2023 Jun 08;11(12):. (PMID: 37372802)
Pediatr Radiol. 2022 Oct;52(11):2087-2093. (PMID: 34117522)
Br J Radiol. 2020 Apr;93(1108):20190840. (PMID: 31821024)
Lancet Digit Health. 2022 Jul;4(7):e507-e519. (PMID: 35750400)
Diagnostics (Basel). 2023 Aug 25;13(17):. (PMID: 37685300)
Eur Radiol. 2024 Mar;34(3):2096-2109. (PMID: 37658895)
J Breast Cancer. 2023 Oct;26(5):405-435. (PMID: 37926067)
J Breast Cancer. 2022 Apr;25(2):147. (PMID: 35506581)
JAMA Oncol. 2020 Oct 1;6(10):1581-1588. (PMID: 32852536)
Commun Med (Lond). 2022 Oct 27;2:133. (PMID: 36310650)
JAMA Netw Open. 2020 Mar 2;3(3):e200265. (PMID: 32119094)
Diagnostics (Basel). 2023 Jun 13;13(12):. (PMID: 37370936)
Eur Radiol. 2021 Nov;31(11):8682-8691. (PMID: 33948701)
Lancet Digit Health. 2020 Mar;2(3):e138-e148. (PMID: 33334578)
Breast. 2022 Mar;62 Suppl 1:S3-S6. (PMID: 35219542)
Radiology. 2023 May;307(4):e222176. (PMID: 37129490)

Date Created: 20250507 Date Completed: 20250507 Latest Revision: 20250509

20250509

PMC12057908

10.1371/journal.pone.0322925

40333871