The role of Artificial Intelligence in detecting breast lesions using ultrasound - Ziemińska, Daria; Motolko, Karina; Burczyk, Rafał; Duszyński, Konrad; Tokarczyk, Elżbieta; Michalska, Martyna; Łabuda, Adam (Quality in Sport, 2025)
Introduction and objective: Breast cancer is the most diagnosed cancer and the second leading cause of cancer deaths in women globally, with rising cases and mortality. Early detection via mammography, ultrasound, or MRI is vital, with ultrasound excelling in dense breast tissue due to its safety and accuracy. Review methods: A literature review utilizing databases like Scopus, Google Scholar, and PubMed, with keywords such as "AI use in radiology" and "BI-RADS scale" underscores the need for advancements in understanding and managing graft rejection. Brief knowledge status: AI develops systems that simulate human intelligence, excelling in breast imaging by detecting patterns and providing accurate results. Machine learning (ML) and deep learning (DL) drive advances, with DL’s CNNs leading in image analysis. AI aids BI-RADS lesion classification, ultrasound lesion detection, lymph node analysis, and treatment response prediction, often surpassing radiologists. Its future relies on real-world validation, improved outcomes, and clinical integration. Discussion: The integration of artificial intelligence (AI) into breast imaging marks a transformative leap in diagnostic radiology, enhancing precision, efficiency, and scalability. Driven by advancements in machine learning (ML) and deep learning (DL), AI excels in analyzing complex datasets. However, its clinical adoption requires addressing key considerations with a nuanced approach. Summary: In conclusion, AI holds immense promise in breast imaging, poised to redefine the field through enhanced diagnostic capabilities and clinical utility. Continued advancements and validation efforts will ensure its broader acceptance and sustained impact in medical imaging.