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 significa
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