CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification
In the rapidly evolving field of healthcare technology, the accuracy of electronic health records (EHRs) is paramount, particularly when it comes to discharge summaries. A recent paper, identified as arXiv:2605.03476v1, introduces CuraView, a multi-agent framework aimed at enhancing the reliability of information extracted from these records. The framework specifically addresses the critical issue of faithfulness hallucinations—statements generated by large language models (LLMs) that may contradict actual source data, posing risks to patient safety.
The challenge of extracting pertinent information from lengthy EHRs is compounded by the labor-intensive nature of manual processing. While LLMs have the potential to improve efficiency in generating discharge summaries, their propensity for generating inaccurate statements, known as hallucinations, necessitates advanced solutions. CuraView was developed to mitigate these risks by integrating a robust detection and verification system.
Overview of CuraView
CuraView employs a unique approach by constructing a GraphRAG-based knowledge graph from patient-level EHRs. This graph serves as the backbone for a closed-loop generation-detection pipeline, allowing for sentence-level evidence retrieval and classification. The evidence is categorized into four grades based on the level of support provided, ranging from strong support to direct contradiction:
- E1: Strong support for the statement
- E2: Moderate support for the statement
- E3: Weak support or inconclusive evidence
- E4: Direct contradiction of the statement
This structured approach not only aids in identifying inaccuracies but also yields interpretable evidence chains that enhance transparency in clinical documentation.
Evaluation and Results
The effectiveness of CuraView was evaluated using a subset of 250 patients from the Discharge-Me benchmark, with 50 patients designated for testing. The results demonstrate a significant improvement in the detection of faithfulness hallucinations. Utilizing a fine-tuned Qwen3-14B detection model, CuraView achieved an F1 score of 0.831 on the safety-critical E4 metric, boasting a recall of 90.9% and precision of 76.5%. Furthermore, the model attained an F1 score of 0.823 on the combined E3 and E4 metrics, representing a remarkable 50.0% relative improvement over baseline models, including RAGTruth-style and QAGS-style methodologies.
Implications for Clinical Documentation
The results underline the importance of evidence-chain-based graph retrieval verification in enhancing the factual reliability of clinical documentation. By providing structured evidence that can be reused for model training and distillation, CuraView paves the way for more accurate and reliable EHR content. This is particularly crucial in an era where the integrity of patient information is directly linked to patient safety and quality of care.
In conclusion, CuraView represents a significant advancement in the application of AI to healthcare, addressing the dual challenges of efficiency and accuracy in clinical documentation. As the healthcare industry continues to embrace technological innovations, frameworks like CuraView could play a pivotal role in safeguarding patient health and improving the overall quality of care.
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