Building Trust in AI: Explainable Solutions for Spine MRI
Explore how Cadabra Studio tackled the challenge of creating an AI solution for spine MRI that supports radiologists while fostering trust and adoption in clinical settings. Our approach centered around designing a specialized AI pipeline involving U-Net, multi-label classifiers, and RegNetY32GF to process lumbar spinal stenosis efficiently. We emphasized transparency through Grad-CAM visualizations and other explainability features, ensuring that clinicians understand and trust AI outputs. Our user-centric design, built in collaboration with doctors, seamlessly integrates into existing workflows. While our solution shows promising adoption, we faced the tradeoff between latency and transparency, introducing dual operating modes to empower clinician choice. As we pilot our solution further, the question of optimizing explainability versus speed in healthcare AI remains pertinent. How do we measure trust, and when does clarity outweigh rapidity? Let's discuss these crucial issues and share insights on integrating AI into medical practice.
Building Trust in AI: Explainable Solutions for Spine MRI
- 1. What We Learned Building Trust in AI for Spine MRI with Explainability Contact us: q https://cadabra.studio 0 hello@cadabra.studio More insights: q cadabra.studio Building Explainable AI for Spine MRI (Medium) l AI-ready UX Blocks for Medical Imaging (Notion)
- 2. Overcoming Variability in Spine MRI Interpretation Importance of standardized, AI-powered analysis Addressing the challenge of diagnostic inconsistency in radiology Our AI-ready UX architecture enables consistency even in complex clinical settings
- 3. Designing a Purpose-Built AI Pipeline 1 Three-stage convolutional architecture 2 U-Net, multi-label classifiers, RegNetY32GF for precision 3 Pipeline engineered with contextual intelligence to serve real-world clinician needs
- 4. The Value of Explainability in AI Models Transparent AI, not black-box solutions Grad-CAM visualizations and overlay masks With contextual intelligence, we design models clinicians can understand and trust
- 5. User-Centric Design for Clinician Adoption Collaboration with doctors for optimal UX Seamless UI integration into clinical workflows Our UX-navigation for AI-businesses keeps clinicians in control at every step
- 6. Balancing Speed with Transparency Latency vs explainability tradeoffs in high- volume settings Dual operating modes: quick read and full XAI
- 7. Insights from Clinical Pilots and Adoption Rates High AUROC score and measurement accuracy Higher Explainable UIs drive higher system usage by clinicians
- 8. What Would You Do? We believe we can reframe software delivery from the ground up, where every decision, tool, and interaction is guided by contextual intelligence. Reflect on balancing speed vs explainability in AI How can trust be measured in clinical AI? Invite comments or connections: 0 hello@cadabra.studio q https://cadabra.studio Learn more: Building Explainable AI for Spine MRI (Medium) l AI-ready UX Blocks for Medical Imaging (Notion)