Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
arXiv:2606.12702v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical systems, making it essential to evaluate the real-world utility of these systems. However, static benchmarks tend to measure correctness rather than user acceptance, aggregate performance across queries, and require densely annotated datasets -- leading to major blind spots for evaluating clinical systems. In this work, we perform a deployment-centered evaluation of an LLM system embedded within electronic health records at an academic medical center, where user feedback is sp
The increasing integration of LLMs into critical systems like healthcare makes robust, deployment-centric evaluation methods essential, moving beyond traditional benchmarks.
This development highlights the critical need for systems that evaluate AI not just for accuracy but for real-world user acceptance and safety in sensitive domains like clinical practice.
The focus of AI evaluation shifts towards anticipating and mitigating query-level rejection risks in deployed systems, rather than solely relying on aggregate benchmark performance.
- · AI safety researchers
- · Healthcare providers
- · Clinical AI system developers
- · Patients
- · Developers relying solely on static benchmarks
- · Early monolithic LLM integration strategies
Improved trust and adoption of AI in clinical settings as systems become more robust to real-world edge cases.
New standards and regulations emerging for real-world deployment evaluation of AI in critical sectors.
A potential shift in AI development methodologies toward 'human acceptance first' design principles in highly sensitive applications.
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Read at arXiv cs.AI