arXiv:2604.10389v2 Announce Type: replace Abstract: Terminology substitution errors in clinical notes, where one medical term is replaced by a linguistically valid but clinically different term, pose a persistent challenge for automated error detection in healthcare. We introduce BLUEmed, a multi-agent debate framework augmented with hybrid Retrieval-Augmented Generation (RAG) that combines evidence-grounded reasoning with multi-perspective verification for clinical error detection. BLUEmed decomposes each clinical note into focused sub-queries, retrieves source-partitioned evidence through de
The increasing sophistication of multi-agent AI systems and RAG techniques is enabling more complex and reliable applications, making advanced clinical error detection feasible now.
Improving automated clinical error detection is critical for patient safety, reducing healthcare costs, and enhancing the efficiency of medical documentation processes, with direct implications for health insurers and providers.
The ability to reliably detect nuanced terminology errors in clinical notes through AI will significantly reduce human review burdens and improve the accuracy of medical records.
- · Healthcare providers
- · Health insurance companies
- · AI-in-health developers
- · Patients
- · Manual clinical review services
- · Legacy error detection software
Reduced medical malpractice claims and improved patient outcomes due to fewer errors in clinical documentation.
Increased trust and adoption of AI tools within sensitive healthcare workflows, paving the way for broader AI integration.
A shift in medical training curricula to focus more on AI collaboration and oversight rather than manual error detection.
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Read at arXiv cs.CL