Objective:

To explore the role of AI-mediated ultrasonic radiomics in the diagnosis and treatment of triple-negative breast cancer (TNBC), emphasizing its potential to improve patient outcomes.

Key Findings:

• TNBC accounts for 15%-20% of breast cancers and has poor prognosis due to limited treatment options, underscoring the need for innovative diagnostic tools.
• AI-driven ultrasonic radiomics improves diagnostic performance and prognostic assessments, potentially leading to better patient management.
• Challenges include insufficient standardized data protocols, limited model interpretability, and lack of multicenter validation, which must be addressed to realize AI's full potential.

Interpretation:

AI-enhanced ultrasound radiomics shows promise in improving TNBC diagnosis and treatment, but further research is urgently needed to address existing challenges and enhance clinical applicability.

Limitations:

• Insufficient standardized data protocols, which hinder model training and validation.
• Limited interpretability of AI models, making it difficult for clinicians to trust and utilize these tools.
• Lack of rigorous multicenter validation studies, which are essential for establishing clinical relevance.

Conclusion:

Future research should focus on establishing standardized workflows and conducting multicenter studies to validate the clinical value of AI-enhanced radiomics in TNBC, emphasizing the critical role of AI in improving patient outcomes.