Retson Tara A, Eghtedari Mohammad
Department of Radiology, University of California, San Diego, CA 92093, USA.
Diagnostics (Basel). 2023 Jun 21;13(13):2133. doi: 10.3390/diagnostics13132133.
Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve lesion detection through a comparison with prior studies and considerations of symmetry. It also holds promise in ultrasound analysis and automated whole breast ultrasound, areas marked by unique challenges. AI's potential utility also extends to administrative tasks such as MQSA compliance, scheduling, and protocoling, which can reduce the radiologists' workload. However, adoption in breast imaging faces limitations in terms of data quality and standardization, generalizability, benchmarking performance, and integration into clinical workflows. Developing methods for radiologists to interpret AI decisions, and understanding patient perspectives to build trust in AI results, will be key future endeavors, with the ultimate aim of fostering more efficient radiology practices and better patient care.
人工智能（AI）在乳腺钼靶摄影中的应用已受到广泛关注；然而，人工智能有潜力彻底改变乳腺成像的其他方面，而不仅仅是简单的病变检测。人工智能有潜力通过将传统因素与影像相结合来加强风险评估，并通过与先前研究进行比较以及考虑对称性来改善病变检测。它在超声分析和全乳自动超声检查方面也很有前景，这些领域面临着独特的挑战。人工智能的潜在用途还扩展到诸如符合乳腺影像质量标准（MQSA）、日程安排和制定检查方案等管理任务，这可以减轻放射科医生的工作量。然而，在乳腺成像中的应用在数据质量和标准化、可推广性、基准性能以及融入临床工作流程方面面临限制。开发让放射科医生解读人工智能决策的方法，以及了解患者的观点以建立对人工智能结果的信任，将是未来的关键工作，最终目标是促进更高效的放射学实践和更好的患者护理。
