Transitioning to expert-in-the-loop AI explainability in orthopaedic practice: Opportunities, challenges, and outlook

Artificial intelligence (AI) is demonstrating very successful applications in orthopaedics, ranging from medical imaging interpretation and risk prediction to surgical planning and post operative monitoring. Yet, many AI systems remain difficult to trust in real-world clinical workflows because they are often optimized for standalone accuracy rather than clinically useable transparency, human AI teamwork, and safe escalation when the AI models are uncertain. Experts-in-the-loop explainability re frames explainable AI(XAI) as a workflow capability; orthopaedic surgeons, musculoskeletal radiologists, fellows, and allied domain experts actively shape how explanations in AI are generated, interpreted, audited, and acted upon. This article outlines opportunities where experts-in-the-loop XAI can improve shared decision accountability, discusses practical challenges (e.g., unreliable saliency maps, explanation-induced cognitive bias, drift, interface burden, and unclear medico legal responsibility), and highlights an outlook for deploying explainable, transparent, auditable, and regulation-ready AI powered orthopaedic practices. Aligning AI explainability with expert oversight and human factors is not optional; it is essential for trustworthy orthopaedic AI integration, where decisions carry high clinical stakes.