VeriSim: A Configurable Framework for Stress-Testing Medical AI Under Patient Communication Noise
Medical large language models are typically evaluated on idealized patient cases that do not reflect how real patients communicate. We introduce VeriSim, a patient simulation framework that injects controllable noise along six clinically grounded communication dimensions while substantially preserving each patient's medical record. Truth adherence is supported by a verifier that extracts atomic claims from each candidate utterance and judges them against a UMLS-grounded vector index built with BioLORD embeddings, using the retrieved atoms' structured clinical metadata (e.g., drug class, anatomical site, treats-condition relations) rather than surface-text similarity alone. Across seven op...
| Authors | Sina Mansouri, Mohit Marvania, Vibhavari Ashok Shihorkar et al. |
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| Primary category | cs.AI |
| Published | 12 April 2026 |
| Last updated at source | 9 September 2026 |
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