Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review
<p dir="ltr">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...
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2025
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| _version_ | 1864513521302110208 |
|---|---|
| author | Daksh Dave (17949239) |
| author2 | Adnan Akhunzada (20151648) Nikola Ivković (22331170) Sujan Gyawali (23718915) Korhan Cengiz (19450537) Adeel Ahmed (11823440) Ahmad Sami Al-Shamayleh (17541495) |
| author2_role | author author author author author author |
| author_facet | Daksh Dave (17949239) Adnan Akhunzada (20151648) Nikola Ivković (22331170) Sujan Gyawali (23718915) Korhan Cengiz (19450537) Adeel Ahmed (11823440) Ahmad Sami Al-Shamayleh (17541495) |
| author_role | author |
| dc.creator.none.fl_str_mv | Daksh Dave (17949239) Adnan Akhunzada (20151648) Nikola Ivković (22331170) Sujan Gyawali (23718915) Korhan Cengiz (19450537) Adeel Ahmed (11823440) Ahmad Sami Al-Shamayleh (17541495) |
| dc.date.none.fl_str_mv | 2025-02-19T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.7717/peerj-cs.2476 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Diagnostic_test_accuracy_of_AI-assisted_mammography_for_breast_imaging_a_narrative_review/31995150 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Information and computing sciences Computer vision and multimedia computation Machine learning Breast cancer Mammography Artificial intelligence Medical imaging Health care |
| dc.title.none.fl_str_mv | Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.2476" target="_blank">https://dx.doi.org/10.7717/peerj-cs.2476</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6b9f95293e1b8f86ef4c27d387f32cf7 |
| identifier_str_mv | 10.7717/peerj-cs.2476 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/31995150 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative reviewDaksh Dave (17949239)Adnan Akhunzada (20151648)Nikola Ivković (22331170)Sujan Gyawali (23718915)Korhan Cengiz (19450537)Adeel Ahmed (11823440)Ahmad Sami Al-Shamayleh (17541495)Biomedical and clinical sciencesClinical sciencesInformation and computing sciencesComputer vision and multimedia computationMachine learningBreast cancerMammographyArtificial intelligenceMedical imagingHealth care<p dir="ltr">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.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.2476" target="_blank">https://dx.doi.org/10.7717/peerj-cs.2476</a></p>2025-02-19T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.7717/peerj-cs.2476https://figshare.com/articles/journal_contribution/Diagnostic_test_accuracy_of_AI-assisted_mammography_for_breast_imaging_a_narrative_review/31995150CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/319951502025-02-19T03:00:00Z |
| spellingShingle | Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review Daksh Dave (17949239) Biomedical and clinical sciences Clinical sciences Information and computing sciences Computer vision and multimedia computation Machine learning Breast cancer Mammography Artificial intelligence Medical imaging Health care |
| status_str | publishedVersion |
| title | Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review |
| title_full | Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review |
| title_fullStr | Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review |
| title_full_unstemmed | Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review |
| title_short | Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review |
| title_sort | Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review |
| topic | Biomedical and clinical sciences Clinical sciences Information and computing sciences Computer vision and multimedia computation Machine learning Breast cancer Mammography Artificial intelligence Medical imaging Health care |