arXiv:2507.02628v2 Announce Type: replace Abstract: 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 introduces Semantic Data Coverage, employing Large Language Models to
The increasing deployment of AI in clinical settings necessitates robust validation methods as the limitations of current quality assessments become glaringly obvious.
Ensuring the reliability and safety of AI in healthcare is paramount, and this approach addresses a critical bottleneck in deploying trustworthy AI systems.
The proposed 'Medical Data Pecking' methodology offers a scalable and semantically aware way to audit electronic health records, moving beyond manual or syntax-focused checks.
- · AI developers in healthcare
- · Healthcare providers
- · Patients
- · Clinical AI auditors
- · Manual data validation processes
- · AI models trained on inconsistent data
Improved accuracy and trustworthiness of clinical AI applications.
Accelerated adoption of AI in healthcare due to higher data reliability and safety assurances.
Potential for new regulatory frameworks and industry standards emphasizing semantic data auditing for AI in sensitive sectors.
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Read at arXiv cs.LG