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TRUVACE RECORD VERSION
record: TRV-2026-1793
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-18T06:54:39.384214Z
status: published
lens: p_space
sector: health
headline: Transitioning to expert-in-the-loop AI explainability in orthopaedic practice: Opportunities, challenges, and outlook
dek: 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 explainab…
gain_title: (none)
problem_title: Many orthopaedic AI systems are hard to trust in real clinical workflows because they prioritize standalone accuracy over usable transparency, human-AI teamwork and safe escalation when uncertain.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Many orthopaedic AI systems are hard to trust in real clinical workflows because they prioritize standalone accuracy over usable transparency, human-AI teamwork and safe escalation when uncertain.
problem_evidence: many AI systems remain difficult to trust in real-world clinical workflows
quick_read: This peer-reviewed article reviews how AI is already applied across orthopaedics, from reading medical images and predicting risk to planning surgery and monitoring patients after operations. It argues that many of these systems are tuned for accuracy on their own and not for explanations clinicians can use, and it proposes making explainability a workflow function shaped by surgeons, radiologists, fellows and allied experts.

The framing matters because orthopaedic decisions carry high clinical stakes, so a model that cannot be trusted, audited or escalated when uncertain may be hard to deploy responsibly. The piece is a discussion of opportunities and outlook, so it offers no measured patient outcomes, and questions such as medico-legal responsibility are described as unclear and remain open.
limitation: The source is a perspective that outlines opportunities, challenges and an outlook, and it names unresolved issues such as unreliable saliency maps and unclear medico-legal responsibility, without reporting measured outcomes.
tag: Evidence-backed problem
key_points: Named AI uses in orthopaedics include imaging interpretation, risk prediction, surgical planning and post-operative monitoring. | The proposed approach has domain experts shape how AI explanations are generated, interpreted, audited and acted upon. | Listed challenges include unreliable saliency maps, explanation-induced cognitive bias, drift, interface burden and unclear medico-legal responsibility. | The outlook calls for explainable, transparent, auditable and regulation-ready AI-powered orthopaedic practice.
rundown: The authors describe current orthopaedic AI as successful in its applications but limited by a gap between model accuracy and clinical usability. Systems optimized for standalone performance may lack transparency, support for human-AI teamwork and safe escalation pathways when models are uncertain.

The expert-in-the-loop concept treats explainable AI as a capability embedded in clinical workflow rather than an add-on. Orthopaedic surgeons, musculoskeletal radiologists, fellows and allied domain experts take an active role in shaping and auditing explanations.

The article names practical hurdles, including saliency maps that may be unreliable, explanations that can introduce cognitive bias, model drift, added interface burden and unclear medico-legal responsibility. It concludes that aligning explainability with expert oversight and human factors is essential for trustworthy integration.
sources:
- peer_reviewed | Journal of Clinical Orthopaedics and Trauma | https://doi.org/10.1016/j.jcot.2026.103626 | 2026-09-21
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