Sabeghi Paniz, Kinkar Ketki K, Castaneda Gloria Del Rosario, Eibschutz Liesl S, Fields Brandon K K, Varghese Bino A, Patel Dakshesh B, Gholamrezanezhad Ali
Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
Front Radiol. 2024 Sep 5;4:1332535. doi: 10.3389/fradi.2024.1332535. eCollection 2024.
Recent advancements in artificial intelligence (AI) and machine learning offer numerous opportunities in musculoskeletal radiology to potentially bolster diagnostic accuracy, workflow efficiency, and predictive modeling. AI tools have the capability to assist radiologists in many tasks ranging from image segmentation, lesion detection, and more. In bone and soft tissue tumor imaging, radiomics and deep learning show promise for malignancy stratification, grading, prognostication, and treatment planning. However, challenges such as standardization, data integration, and ethical concerns regarding patient data need to be addressed ahead of clinical translation. In the realm of musculoskeletal oncology, AI also faces obstacles in robust algorithm development due to limited disease incidence. While many initiatives aim to develop multitasking AI systems, multidisciplinary collaboration is crucial for successful AI integration into clinical practice. Robust approaches addressing challenges and embodying ethical practices are warranted to fully realize AI's potential for enhancing diagnostic accuracy and advancing patient care.
人工智能（AI）和机器学习的最新进展为肌肉骨骼放射学提供了众多机会，有可能提高诊断准确性、工作流程效率和预测建模能力。人工智能工具能够在许多任务中协助放射科医生，包括图像分割、病变检测等。在骨与软组织肿瘤成像中，放射组学和深度学习在恶性肿瘤分层、分级、预后评估及治疗规划方面显示出前景。然而，在临床转化之前，需要解决标准化、数据整合以及患者数据的伦理问题等挑战。在肌肉骨骼肿瘤学领域，由于疾病发病率有限，人工智能在强大算法开发方面也面临障碍。虽然许多举措旨在开发多任务人工智能系统，但多学科合作对于将人工智能成功整合到临床实践中至关重要。需要采用强有力的方法来应对挑战并体现道德规范，以充分发挥人工智能在提高诊断准确性和推进患者护理方面的潜力。
