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While state-of-the-art models like NeuroBOLT demonstrate mathematical excellence in EEG-to-fMRI synthesis, they often remain clinically opaque. With the EU AI Act classifying medical AI as "high-risk," hospitals cannot deploy "black boxes"; they require systems that are transparent, auditable, and legally compliant. This session presents a "Clinical Auditing System" built within the PyTorch ecosystem, designed to transform opaque deep learning models into transparent "Glass Boxes." I will demonstrate a workflow that backpropagates gradients from high-dimensional 4D fMRI volumes to identify the specific EEG spectral signatures driving those predictions. Key Technical Takeaways: 1. The Audit Layer: Implementing IntegratedGradients (Captum) to verify model fidelity, ensuring predictions stem from valid neural oscillations rather than noise artifacts. 2. Cross-Modal Reasoning: A technical demonstration of mapping 4D volumetric outputs back to 1D EEG frequency bands, enabling the model to "reason" through neurovascular coupling. This presentation is designed for developers seeking to wrap PyTorch models in safety layers that satisfy demands of healthcare regulation.
Medical Student, AI Researcher and Neurotech Founder, ClinExplain
Muhammad Saqib is a 4th-year medical student at Comenius University Bratislava and Founder of Neurosonic Academy. His M.D. thesis explores AI for Sleep Medicine. Leveraging PyTorch and Captum, he builds "Glass Box" auditing frameworks to validate generative neuroimaging models against... Read More →
