This review looks at how artificial intelligence (AI) and advanced ultrasound techniques are being used to study triple negative breast cancer. These technologies, known as ultrasonic radiomics, combine machine learning with detailed imaging to help identify different cancer subtypes.
The research shows that these AI models can help predict important outcomes, such as how long a patient might remain disease-free or their overall survival. By looking at specific textures in and around the tumor, these tools are moving from simple diagnosis toward more complex classification.
However, there are important reasons to be cautious. The current evidence is based on a review of existing research rather than new clinical trials. There is still a lack of standardized protocols, and the models can be difficult for humans to interpret.
Before these tools can be used in regular doctor visits, they need to be tested in large, multi-center studies to ensure they work reliably across different hospitals and populations. For now, this technology remains an area of active development rather than a standard part of care.