An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
arXiv:2606.05357v1 Announce Type: new Abstract: Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI). Materials and Methods: We first developed a deep learning framework to predict MOAKS features directly from knee MRIs and incorporated conformal prediction to provide prediction uncertainty quantification. This uncertainty-aware strategy enables explicit filterin
The proliferation of medical imaging data and advancements in interpretable AI techniques are enabling more complex, large-scale studies in clinical research.
This development represents progress in leveraging AI for nuanced medical diagnostics and research, moving beyond black-box models towards explainable, trustworthy systems that can directly inform treatment and understanding of diseases.
The ability to conduct large-scale, longitudinal studies associating structural changes with pain using interpretable AI will enhance understanding of chronic conditions like osteoarthritis, potentially accelerating drug discovery and personalized treatment strategies.
- · Medical AI researchers
- · Pharmaceutical companies
- · Healthcare providers
- · Patients with chronic pain
- · Traditional drug discovery models
- · Medical diagnostic methods reliant solely on human interpretation
More precise and earlier diagnosis of osteoarthritis and related pain conditions.
Development of targeted therapies and interventions based on better understanding of structure-pain relationships.
Potential for AI to transform clinical trial design and patient stratification for various chronic diseases beyond osteoarthritis.
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Read at arXiv cs.AI