arXiv:2605.28129v1 Announce Type: new Abstract: Clinical foundation models are evaluated with factual or exam-style medical QA, but treatment decisions must change when patient context changes. We introduce ClinPivot, an auditable treatment-decision benchmark built from biomedical relations and pivoted patient contexts. ClinPivot asks whether models change treatment choices when new clinical constraints shift the action space. We find that strong medical QA performance does not reliably predict decision-making performance: frontier models and task-adapted Qwen variants often fail to change dec
The proliferation of medical AI models necessitates rigorous evaluation beyond factual recall to assess their practical utility in dynamic clinical settings.
A strategic reader should care because this research highlights a critical gap in AI's clinical application, indicating that current benchmarks may not accurately predict real-world decision-making performance.
The criteria for evaluating clinical AI models are shifting from mere factual accuracy to a more nuanced assessment of their adaptability and reliability in complex, context-dependent treatment decisions.
- · AI ethics and safety researchers
- · Healthcare providers proficient in model validation
- · Patients receiving AI-augmented care
- · Developers of uncritical large medical models
- · Clinical AI products lacking robust decision-making benchmarks
- · Healthcare systems adopting models based solely on QA performance
Clinical AI models require new, advanced benchmarks that test their ability to adapt treatment recommendations based on changing patient contexts.
This will drive a focus on developing more sophisticated AI architectures capable of nuanced, context-aware reasoning rather than just information retrieval.
The medical AI market will bifurcate between models proven to responsibly influence treatment decisions and those relegated to lower-stakes informational roles.
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Read at arXiv cs.AI