arXiv:2605.30590v1 Announce Type: new Abstract: Two clinical AI systems can score nearly identically on coverage-based rubrics yet behave radically differently when their patient inputs change: one updates its recommendations to match the new clinical signal, while the other produces the same output regardless. We introduce the Causal Sensitivity Score (CSS), a pre-registered interventional metric that mutates oncology tumor-board cases along five clinically meaningful dimensions - biomarker flips, prior-treatment failures, biomarker removals, surgery-status changes, and stage perturbations -
The proliferation of clinical AI systems necessitates robust and granular evaluation methods beyond simple accuracy scores to understand their reliability and potential for unintended consequences.
This development introduces a critical methodology for evaluating the nuanced capabilities and safety of AI in high-stakes fields like medicine, moving beyond superficial performance metrics to causal understanding.
The criteria for assessing clinical AI systems will shift from simple coverage-based rubrics to more sophisticated causal sensitivity scores, revealing hidden behavioral profiles.
- · Patients
- · Clinical AI developers focused on robustness
- · Healthcare providers
- · AI safety researchers
- · Clinical AI developers with brittle models
- · Evaluation methods relying solely on aggregate metrics
- · Early-stage clinical AI with poor adaptability
Clinical AI systems will undergo more rigorous and interventional testing to prove their reliability in dynamic healthcare environments.
This will drive a demand for more causally-aware AI architectures and development practices in critical applications.
Improved safety and reliability could accelerate the adoption of AI agents in clinical settings, demanding new regulatory frameworks tailored to explainable causality.
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Read at arXiv cs.LG