arXiv:2504.06299v2 Announce Type: replace-cross Abstract: Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretability remains limited. We present multimodal Deep Transformation Models (DTMs) combining statistical approaches and neural networks to achieve strong predictive performance while preserving interpretability for tabular data. A key contribution of this work is the adaption of the xAI methods Grad-CAM and Occlusion to DTMs relying on 3D CNNs, enabling interpretation of the image branch through the gener
The increasing adoption of AI in critical fields like medicine necessitates greater transparency and interpretability, leading to a surge in explainable AI research.
Improved interpretability in medical AI models can enhance clinician trust and accelerate the deployment of AI for more accurate and explainable patient outcomes, particularly in conditions like stroke.
This development offers a refined methodology for understanding complex multimodal AI predictions in clinical settings, potentially bridging the gap between raw AI output and actionable medical insight.
- · Healthcare sector
- · Patients with stroke
- · AI developers
- · Medical researchers
- · Black box AI models
- · Clinical decision-making without interpretability
Diagnostic and prognostic tools in medicine become more reliable and widely adopted due to increased explainability.
Regulatory bodies may begin to mandate specific levels of explainability for AI models deployed in critical healthcare applications.
The demand for 'explainable AI' specialists and research will grow significantly across various industries beyond healthcare.
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Read at arXiv cs.LG