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.
Keyphrases
- artificial intelligence
- deep learning
- big data
- machine learning
- soft tissue
- convolutional neural network
- high resolution
- clinical practice
- electronic health record
- healthcare
- bone mineral density
- palliative care
- quality improvement
- working memory
- computed tomography
- lymph node metastasis
- squamous cell carcinoma
- bone loss
- body composition
- decision making
- mass spectrometry
- data analysis
- fluorescence imaging