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

arXiv 2607.25038 ↗First posted 27 Jul 2026cs.IR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 12 references that are in the map. First posted on arXiv 27 Jul 2026.

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References · 2

  1. 2025CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Recordscited by 4
  2. 2025Demo: Guide-RAG: Evidence-Driven Corpus Curation for Retrieval-Augmented Generation in Long COVIDcited by 3

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  3. 2026LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making
  4. 2026ClinLens: Towards Long-Horizon Coding Agents for Longitudinal Multimodal Clinical Data Science
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  6. 2023Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature
  7. 2026CCS: Clinical Consensus Selection for Radiology Report Generation
  8. 2025Medical AI Consensus: A Multi-Agent Framework for Radiology Report Generation and Evaluation