Evaluation of retrieval-augmented generation (RAG) systems traditionally focuses on verifying that model claims are supported by retrieved documents, without checking whether those documents actually correspond to the queried entity. This gap gives rise to a phenomenon we call 'deceptive grounding' (DG): an entity-attribution failure that goes unnoticed by standard faithfulness, hallucination, and citation metrics, because every claim comes from a real document—but about the wrong entity. In the clinical domain, a RAG system can respond with apparent precision about drug X while citing studies on drug Y, without any automated check detecting it. This issue not only compromises the reliability of healthcare applications but also opens a critical gap in patient safety.
Recent research, such as a controlled factorial benchmark across 13 models, reveals DG rates ranging from 8% to 87% under peak adversarial conditions. Biomedical fine-tuned models reached up to 86.7%, demonstrating that domain specialization amplifies the failure rather than mitigating it. A controlled ablation identifies the underlying mechanism: when entity-specific clinical evidence is removed from retrieved documents, entity-attribution failure disappears entirely, shifting all errors to confabulation. Both failure modes respond to the same trigger but take different paths. In a production measurement over 740 drug-disease pairs, an overall 7.8% DG was found in a deployed RAG system, rising to 13.6% for recently approved drugs. Entity-attribution verification—checking that cited evidence applies to the queried entity—detects DG with 97.0% precision and 98.7% recall (IPW-adjusted human gold standard), yet no existing framework implements it.
From a technical and business perspective, this finding underscores the need to incorporate additional verification layers in RAG systems, especially in critical sectors like healthcare. At Q2BSTUDIO, we understand that artificial intelligence applied to medicine must be not only accurate but also attributable. That is why, when developing AI solutions for the clinical sector, we integrate entity-verification mechanisms that go beyond mere textual faithfulness. Our approach combines custom software development with robust cloud architectures (AWS/Azure) to manage large volumes of clinical documentation, and we employ cybersecurity techniques to protect sensitive patient data. Continuous monitoring through BI/Power BI enables real-time detection of DG patterns, while specialized AI agents can perform attribution checks before delivering a final response.
The lesson is clear: trust in clinical RAG systems cannot rely solely on superficial metrics. Deceptive grounding is an invisible failure that can only be countered by systematic verification of the alignment between the queried entity and the retrieved evidence. Companies leading digital transformation in healthcare, such as Q2BSTUDIO, are called to incorporate these safeguards into their software architectures, ensuring that every care-related response is not only factual but truly belongs to the correct patient and treatment. The future of AI in medicine depends on it.
