Uncertainty quantification and explainable AI in orthopaedic imaging: A timely call to action.

Area of Science:

• Orthopaedic Imaging
• Artificial Intelligence
• Medical AI

Background:

• Deep learning models show high accuracy in orthopaedic imaging tasks like fracture detection and osteoarthritis grading.
• Clinical trust and adoption of AI in orthopaedics are hindered because accuracy alone is insufficient.
• Current AI models often lack transparency, providing predictions without explaining reasoning or quantifying uncertainty.

Purpose of the Study:

• To advocate for the integration of uncertainty quantification and explainable AI in orthopaedic imaging.
• To highlight the necessity of these advancements for clinical trust and safe adoption.
• To bridge the gap between AI innovation and practical orthopaedic workflows.

Main Methods:

• Discusses the role of uncertainty quantification in identifying unreliable AI predictions.
• Explains how explainable AI (XAI) techniques enhance transparency in AI model reasoning.
• Emphasizes the combined potential of these methods for trustworthy AI.

Main Results:

• Uncertainty quantification can prompt confirmatory testing or human oversight for AI predictions.
• Explainable AI enables surgeons and radiologists to better interpret AI outputs.
• The integration of these methods moves AI beyond accuracy towards accountability.

Conclusions:

• Uncertainty-aware and explainable AI are essential for trustworthy AI in orthopaedics.
• These advancements are critical for the safe integration of AI into clinical practice.
• The orthopaedic community must act now to embrace these crucial AI components.