arXiv:2606.06647v1 Announce Type: new Abstract: Objective. EEG foundation models (FMs) report strong accuracy on clinical resting-state EEG. However, high accuracy under subject-disjoint cross-validation remains ambiguous: it can reflect a genuine clinical biomarker, or subject-identity features that correlate with the label. We name this the Identity Trap and ask whether it can be diagnosed at the representation level before fine-tuning. Approach. We propose FMScope, a frozen-representation protocol packaging five diagnostics: variance decomposition, subject-axis erasure, aperiodic 1/f ablati
The proliferation of AI foundation models in sensitive medical domains like EEG is prompting a critical assessment of their inherent biases and diagnostic integrity.
This research highlights a fundamental challenge in medical AI model trustworthiness, where high accuracy can mask spurious correlations rather than true clinical insight.
The proposed FMScope diagnostic protocol offers a new method to evaluate the integrity of EEG foundation models, potentially shifting development priorities towards more robust and interpretable representations.
- · AI ethicists
- · Medical AI researchers
- · Patients receiving EEG-based diagnostics
- · Developers of transparent AI diagnostic tools
- · Overconfident AI model developers
- · Clinical diagnostics relying on unchecked AI
- · Black-box AI models in healthcare
Medical AI development will increasingly integrate diagnostic audits for representational integrity beyond simple accuracy metrics.
Clinical adoption of AI-powered diagnostics will be contingent on their ability to demonstrate genuine biomarker correlation rather than 'identity traps'.
The principles of diagnostic auditing developed for EEG could be generalized to other complex medical data modalities, influencing regulatory frameworks for AI in healthcare.
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Read at arXiv cs.LG