[Paper Review] Explainable AI in Orthopedics: Challenges, Opportunities, and Prospects

Soheyla Amirian, Luke Carlson+5 more|arXiv (Cornell University)|Aug 9, 2023

Artificial Intelligence in Healthcare and Education4 citations

TL;DR

This paper proposes a multidisciplinary framework for integrating Explainable AI (XAI) in orthopedics to enhance transparency, trust, and clinical adoption of AI models. By combining user-centric design, pilot testing, continuous feedback, and ethical guidelines, the authors demonstrate that XAI can improve diagnostic accuracy, reduce bias, and support shared decision-making in orthopedic care.

ABSTRACT

While artificial intelligence (AI) has made many successful applications in various domains, its adoption in healthcare lags a little bit behind other high-stakes settings. Several factors contribute to this slower uptake, including regulatory frameworks, patient privacy concerns, and data heterogeneity. However, one significant challenge that impedes the implementation of AI in healthcare, particularly in orthopedics, is the lack of explainability and interpretability around AI models. Addressing the challenge of explainable AI (XAI) in orthopedics requires developing AI models and algorithms that prioritize transparency and interpretability, allowing clinicians, surgeons, and patients to understand the contributing factors behind any AI-powered predictive or descriptive models. The current contribution outlines several key challenges and opportunities that manifest in XAI in orthopedic practice. This work emphasizes the need for interdisciplinary collaborations between AI practitioners, orthopedic specialists, and regulatory entities to establish standards and guidelines for the adoption of XAI in orthopedics.

Motivation & Objective

• Address the critical gap in explainability that hinders AI adoption in orthopedic clinical practice.
• Overcome challenges related to model transparency, interpretability, and trust among clinicians, patients, and regulators.
• Develop a collaborative framework involving AI scientists, clinicians, regulators, and patients to guide XAI implementation.
• Ensure regulatory compliance and ethical use of AI by aligning XAI with data protection laws and fairness principles.
• Promote clinical integration through pilot projects, continuous feedback, and comprehensive training for healthcare professionals.

Proposed method

• Categorize XAI methods into local, global, and counterfactual explanations to interpret individual predictions, model-wide feature importance, and outcome sensitivity.
• Implement user-centric design by involving clinicians and patients in the development of XAI systems to ensure usability and actionable explanations.
• Conduct small-scale pilot projects in specific orthopedic applications (e.g., pain progression using multi-modal data) to test feasibility and user acceptance.
• Integrate continuous feedback loops to iteratively improve XAI systems based on clinical workflow integration and user needs.
• Establish ethical guidelines for data privacy, fairness, informed consent, and bias mitigation in AI-driven orthopedic decision-making.
• Provide comprehensive training for healthcare professionals on interpreting XAI outputs, understanding model limitations, and applying explanations in clinical contexts.

Experimental results

Research questions

• RQ1 How can XAI methods improve transparency and trust in AI-driven decisions within orthopedic care?
• RQ2 What are the key challenges to implementing XAI in clinical orthopedic settings, and how can they be addressed through collaboration?
• RQ3 How can XAI be integrated into existing clinical workflows without disrupting patient care or provider routines?
• RQ4 What role do ethical considerations, including bias detection and patient autonomy, play in the responsible deployment of XAI in orthopedics?
• RQ5 How can user feedback and pilot testing inform the scalable and sustainable adoption of XAI in orthopedic institutions?

Key findings

• XAI enhances trust among clinicians and patients by providing interpretable justifications for AI-generated predictions and decisions.
• Local, global, and counterfactual explanation methods enable clinicians to understand feature contributions, detect biases, and explore alternative clinical scenarios.
• Multidisciplinary collaboration among AI researchers, clinicians, regulators, and policymakers is essential for developing adaptable, ethical, and clinically relevant XAI frameworks.
• Pilot projects focused on specific orthopedic applications (e.g., pain progression) demonstrate the feasibility and user acceptance of XAI in real-world settings.
• Continuous feedback and iterative improvement are critical for refining XAI systems and ensuring long-term integration into clinical workflows.
• Comprehensive training for healthcare professionals improves their ability to interpret and act on XAI explanations, increasing model adoption and clinical utility.