Artificial intelligence in musculoskeletal applications: a primer for radiologists

Area of Science:

• Radiology
• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Artificial intelligence (AI) is an evolving field with increasing applications in medical imaging.
• Understanding AI, machine learning (ML), and deep learning (DL) is crucial for modern medical practice.

Purpose of the Study:

• To provide a foundational understanding of AI, ML, and DL for radiologists.
• To review algorithms commonly employed in musculoskeletal radiology.
• To discuss practical considerations and challenges in AI implementation within this subspecialty.

Main Methods:

• Review of AI, ML, and DL concepts.
• Literature review of AI applications in musculoskeletal radiology.
• Discussion of common AI algorithms and their use cases.

Main Results:

• AI encompasses ML and DL, which utilize algorithms for image analysis and interpretation.
• Specific algorithms are relevant to musculoskeletal radiology, aiding in tasks like fracture detection and disease classification.
• Key practices and potential issues, such as data bias and interpretability, are identified.

Conclusions:

• Radiologists need to understand AI fundamentals to leverage its potential in musculoskeletal imaging.
• Familiarity with AI algorithms and their applications can enhance diagnostic accuracy and efficiency.
• Addressing practical challenges is essential for the successful integration of AI in clinical radiology.