{
  "abstract": "Objective General-purpose large language models (LLMs) show promise for medical question answering (QA) but face oncology-specific challenges, including hallucination, weak evidence attribution, and limited handling of sensitive patient queries. This study develops an evidence-grounded, patient-oriented oncology QA system integrating hybrid retrieval and specialised multi-agent processing to improve evidence attribution, safety, and empathetic communication. Methods The proposed OncoAgentic-RAG is a collaborative multi-agent hybrid retrieval-augmented generation framework for oncology QA. Its knowledge base comprises standard oncology textbooks and clinical guidelines. Dense ChromaDB and BM25 sparse retrieval are combined for evidence retrieval and fusion in a LangGraph-based multi-agent architecture that incorporates crisis detection, intent orchestration, medical knowledge retrieval, treatment guidance, empathy-aware generation, clinical verification and patient-friendly response synthesis. The system was evaluated on the 1,015-question OncoIntentQA benchmark using retrieval, semantic, faithfulness, relevance, safety, completeness, empathy, and user-friendliness measures, with comparisons against NoRAG, BaselineRAG, and AgenticRAG. Generated responses were additionally assessed by clinical experts for safety and appropriateness. Results OncoAgentic-RAG achieved Precision@5 of 0.8120, Recall@5 of 0.7980, BERTScore F1 of 0.7634, Faithfulness of 0.8146, and Answer Relevance of 0.8625. It achieved a weighted S.C.O.P.E. (Safety, Completeness, Originality, Precision, and Efficiency) score of 4.68/5.0, Empathy Composite of 0.9180, and User-Friendliness of 0.9580. Clinical expert assessment provided additional evaluation of response safety and content. Conclusions Integrating hybrid retrieval with specialised multi-agent processing improved evidence-grounded, source-traceable, and patient-friendly oncology QA within the evaluated benchmark. The findings demonstrate the technical utility of OncoAgentic-RAG for patient-oriented oncology information, while prospective clinical and safety validation remains necessary.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "S Jayasrilakshmi"
    },
    {
      "name": "Ansuman Mahapatra"
    },
    {
      "name": "Ankit Kujur"
    },
    {
      "name": "Choudhari Aniket Baban"
    },
    {
      "name": "Aditya Kumar Singh"
    },
    {
      "name": "Sachin Nandan Mohanty"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9f7xz81efndeg.bib",
  "cited_by": 0,
  "doi": "10.1177/20552076261500454",
  "fulltext_url": "https://doi.org/10.1177/20552076261500454",
  "id": "9f7xz81efndeg",
  "issns": [
    "2055-2076"
  ],
  "landing_url": "https://doi.org/10.1177/20552076261500454",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R858-859.7"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7223216393",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "SAGE Publishing",
  "ris": "https://research.draconitic.com/paper/9f7xz81efndeg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "119e87d53cb7480f9cc9e0bbe3a69e11",
      "source": "doaj",
      "url": "https://doi.org/10.1177/20552076261500454"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "OncoAgentic-RAG: An evidence-grounded multi-agent hybrid retrieval-augmented system for patient-centric clinical oncology question answering",
  "title_text": "OncoAgentic-RAG: An evidence-grounded multi-agent hybrid retrieval-augmented system for patient-centric clinical oncology question answering",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:50Z",
  "url": "https://research.draconitic.com/paper/9f7xz81efndeg",
  "venue": "Digital Health",
  "versions": []
}