AI RESEARCH
A Generative Approach for Semantic Auditing of Electronic Health Records
arXiv CS.LG
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ArXi:2507.02628v2 Announce Type: replace The reliability of clinical artificial intelligence (AI) depends on high-quality data, yet Electronic Health Records are often inconsistent with existing scientific knowledge. Current quality assessments are limited: they either focus on syntax or rely on labor-intensive manual rules to capture semantic nuances. To overcome these scalability barriers, we propose Medical Data Pecking, a methodology that adopts software unit testing principles for medical data validation. It.