arXiv:2606.30313v1 Announce Type: cross Abstract: Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such as RANO. However, most deep learning methods predict response labels directly from imaging features, which limits clinical inspection, verification, and correction. We introduce TRACE, a RANO 2.0-aligned concept bottleneck model for interpretable 4-class glioblastoma response classification on longitudinal 3D MRI. TRACE processes paired baseline and follow-up multimodal MRI scans with a shared 3D v
The development of more interpretable AI models is a major focus as AI integration into critical fields like medicine accelerates, addressing concerns about 'black box' AI.
This development allows for greater confidence and clinical utility in AI-assisted medical diagnostics, potentially improving patient outcomes and accelerating regulatory approval.
The introduction of interpretable concept bottleneck models shifts AI from mere prediction to providing clearer, clinically-aligned reasoning for its diagnostic conclusions.
- · Medical AI developers specializing in interpretability
- · Oncologists and radiologists
- · Patients with glioblastoma
- · Healthcare systems
- · AI models lacking interpretability in clinical settings
- · Traditional, purely image-feature-based diagnostic methods
AI becomes more integrated into longitudinal clinical decision-making protocols for complex diseases.
Increased trust in AI's diagnostic capabilities could lead to wider adoption across other medical specialties and regulatory frameworks.
The methodology for concept bottleneck models could influence the design of interpretable AI across various high-stakes domains beyond medicine.
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Read at arXiv cs.LG