Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints

Synthetic clinical benchmarks for enterprise AI agents can pass existing utility checks and still remain structurally unrealistic, especially in privacy-sensitive healthcare settings where operational data are hard to access. We study how to improve such benchmarks without breaking the downstream utility checks already used in practice. We formulate benchmark revision as utility-constrained realism improvement: dataset changes should increase realism while staying above an operational utility fl

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ethics.ai (6 August 2026), “Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints,” evidence record 17067, https://ethics.ai/record/17067 (originally published by arXiv).

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