AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025300062
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REVIEW ARTICLE

Artificial intelligence versus elastography in characterizing BI-RADS 4 breast nodules: A systematic review and critical appraisal

Atul Kapoor1* ,  Aprajita Kapur2
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1 Department of Radiology, Advanced Diagnostics and Institute of Imaging, Amritsar, Punjab, India
2 Department of Ultrasonography, Advanced Diagnostics and Institute of Imaging, Amritsar, Punjab, India
AIH, 025300062 https://doi.org/10.36922/AIH025300062
Received: 23 July 2025 | Revised: 25 August 2025 | Accepted: 28 August 2025 | Published online: 17 September 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Breast imaging reporting and data system (BI-RADS) category 4 breast lesions represent a heterogeneous category with moderate suspicion of malignancy, which pose significant diagnostic challenges. Both artificial intelligence (AI) and elastography have demonstrated potential adjunctive roles in improving the evaluation of these lesions. Given the increasingly pervasive use of AI in the medical field, a systematic and critical evaluation of its diagnostic efficacy, clinical utility, and practical applications, compared with elastography techniques, is warranted for the assessment of BI-RADS 4 breast nodules. A systematic literature search was conducted across multiple databases from January 2010 to December 2024, and the studies were critically appraised using standardized quality assessment tools (e.g., quality assessment of diagnostic accuracy studies-2). Due to the significant heterogeneity in study populations and methodologies, a narrative synthesis approach with comprehensive critical appraisal was employed. A total of 23 studies met the inclusion criteria for AI assessment (n = 15,847 lesions) and 31 for elastography (n = 12,456 lesions). Critical appraisal revealed significant methodological limitations—67% of the studies had a high risk of bias in patient selection, 45% in index-test conduct, and 56% in flow and timing. Only 25% of the studies were considered high quality. AI systems demonstrated promising diagnostic performance in individual studies (reported area under the curve range: 0.82–0.94), while elastography showed consistent but more modest performance (reported area under the curve range: 0.72–0.87). However, the quality of the evidence was insufficient for a reliable comparative assessment. While both technologies show promise, the existing evidence is limited by significant methodological constraints, precluding reliable comparative conclusions. These gaps highlight the need for high-quality prospective head-to-head comparison studies with standardized protocols and rigorous methodology.

Graphical abstract
Keywords
Breast imaging
Breast imaging reporting and data system category 4
Artificial intelligence
Elastography
Systematic review
Diagnostic accuracy
Critical appraisal
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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