Grounded in Consensus, In Step With Emerging Science: A Consensus-Anchored Multi-Corpus Clinical Chatbot for Long COVID

Abstract:Long COVID (LC) poses a challenge for clinical decision support because relevant evidence is distributed across sources with different update cycles, evidentiary roles, and levels of clinical maturity. We present a clinician-facing chatbot that organizes four sources within a retrieval-augmented workflow: expert-curated consensus guidance, current PubMed literature, registered interventional trials, and evidence from living systematic reviews. Consensus guidance is always included to frame responses, while the remaining sources are retrieved in parallel when selected by the user. In an exploratory automated evaluation on 50 clinician-facing questions, our chatbot showed comparable mean ratings to OpenEvidence, with numerically higher scores and lower score variability in an LLM-judged comparison.

Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)

arXiv:2607.25038 [cs.IR]

(or arXiv:2607.25038v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2607.25038 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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