arXiv:2605.31504v1 Announce Type: new Abstract: Multimodal models in oncology can produce accurate predictions, but accurate prediction does not reveal whether the model has learned biology that is shared across modalities, biology confined to one modality, or spurious correlations that reflect confounders rather than genuine biology. We introduce DECAT, a model-agnostic post-hoc evaluation framework that classifies multimodal representations into four diagnostic scenarios for a given task and modality, using five null-referenced metrics and a rule-based decision procedure. The framework opera
The proliferation of multimodal AI models in sensitive fields like oncology necessitates robust evaluation frameworks to ensure their predictions are based on genuine biological insights, not spurious correlations.
This framework addresses a critical challenge in AI development by providing a diagnostic tool to differentiate between truly intelligent, biologically-grounded AI predictions and those that merely appear accurate due to hidden biases or noise.
The ability to systematically interrogate multimodal AI models for their underlying biological understanding will improve trustworthiness, accelerate scientific discovery, and inform clinical integration, moving beyond mere predictive accuracy.
- · AI ethicists
- · Oncology researchers
- · AI developers focused on explainable AI
- · Patients receiving AI-assisted care
- · AI models reliant on spurious correlations
- · Developers neglecting biological plausibility
This framework allows for more rigorous validation and refinement of multimodal AI models in healthcare settings.
Improved model interpretability and reliability could accelerate drug discovery and personalized treatment plans by focusing on biologically sound predictions.
The principles behind DECAT could extend to other safety-critical AI applications, raising the bar for 'trustworthy AI' across sectors.
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Read at arXiv cs.LG