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Diagnostics
Volume 15
Issue 17
10.3390/diagnostics15172197
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Review

Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice

by
Andrea Lastrucci
1,
Nicola Iosca
1,
Yannick Wandael
1,
Angelo Barra
1,
Renzo Ricci
1,
Jacopo Nori Cucchiari
2,
Nevio Forini
3,
Graziano Lepri
4 and
Daniele Giansanti
5,*
1
Department of Allied Health Professions, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
2
Breast Imaging Unit, Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
3
Dipartimento di Medicina e Chirurgia, Universita’ degli Studi di Perugia, Piazzale Settimio Gambuli, 1, 06129 Perugia, Italy
4
Unità Sanitaria Locale Umbria 1, Via Guerriero Guerra 21, 06127 Perugia, Italy
5
Centre TISP, Istituto Superiore di Sanità, 00161 Rome, Italy
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(17), 2197; https://doi.org/10.3390/diagnostics15172197
Submission received: 13 July 2025 / Revised: 14 August 2025 / Accepted: 18 August 2025 / Published: 29 August 2025
(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)
Versions Notes

Abstract

Background: Breast cancer is still the most common type of cancer worldwide. Advances and the global use of artificial intelligence (AI) have opened up new opportunities to improve diagnostic accuracy and optimize breast cancer screening. This review summarizes the findings from systematic reviews to assess the current situation of AI integration in mammography. Methods: A total of 28 systematic reviews were included and analyzed using a standardized narrative checklist to assess the impact of AI on mammography imaging. Bibliometric analysis and thematic synthesis were used to assess trends, evaluate the performance of AI in different modalities and identify challenges and opportunities for clinical implementation. Results and Discussion: AI technologies show an overall performance comparable to radiologists in terms of sensitivity and specificity, especially when integrated with human interpretation to detect breast cancer in mammography. However, most studies are retrospective, which raises concerns about their generalizability to real-world clinical settings. Key limitations include potential dataset bias—often stemming from the over-representation of specific imaging equipment or clinical environments—limited ethnic and demographic diversity, the lack of model explainability that hinders clinical trust, and an unclear or evolving legal and regulatory framework that complicates integration into standard practice. Conclusions: AI has the potential to transform mammography screening, but its integration into the real world requires prospective validation, ethical safeguards and robust regulatory oversight. Coordinated international efforts are essential to ensure that AI is used safely, fairly and effectively in breast cancer diagnostics.
Keywords: mammography; artificial intelligence; breast cancer mammography; artificial intelligence; breast cancer

Share and Cite

MDPI and ACS Style

Lastrucci, A.; Iosca, N.; Wandael, Y.; Barra, A.; Ricci, R.; Nori Cucchiari, J.; Forini, N.; Lepri, G.; Giansanti, D. Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice. Diagnostics 2025, 15, 2197. https://doi.org/10.3390/diagnostics15172197

AMA Style

Lastrucci A, Iosca N, Wandael Y, Barra A, Ricci R, Nori Cucchiari J, Forini N, Lepri G, Giansanti D. Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice. Diagnostics. 2025; 15(17):2197. https://doi.org/10.3390/diagnostics15172197

Chicago/Turabian Style

Lastrucci, Andrea, Nicola Iosca, Yannick Wandael, Angelo Barra, Renzo Ricci, Jacopo Nori Cucchiari, Nevio Forini, Graziano Lepri, and Daniele Giansanti. 2025. "Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice" Diagnostics 15, no. 17: 2197. https://doi.org/10.3390/diagnostics15172197

APA Style

Lastrucci, A., Iosca, N., Wandael, Y., Barra, A., Ricci, R., Nori Cucchiari, J., Forini, N., Lepri, G., & Giansanti, D. (2025). Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice. Diagnostics, 15(17), 2197. https://doi.org/10.3390/diagnostics15172197

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