CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars
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Computer Science > Artificial Intelligence
arXiv:2609.29075 (cs)
[Submitted on 24 Sep 2026]
Title:CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars
Authors:Vani Seth, Mohammad Beheshti, Anirudh Kambhampati, Vishwa Bhayani, Lucinda Ham, Prasad Calyam, Iris Zachary
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Abstract:Cancer registrars, including Oncology Data Specialists (ODSs), must interpret complex and frequently updated coding and staging standards. We developed CRISS (Cancer Registry Intelligent Support System), a retrieval-augmented generation (RAG) conversational assistant that provides rapid, citation-supported access to registry guidance. This study evaluated whether CRISS could (1) support accurate and citation-supported responses, (2) improve access to and interpretation of relevant guidance, and (3) support training/helpdesk use while preserving human oversight of final abstraction decisions. We built a domain-specific knowledge base from national cancer registry standards, segmented into metadata-tagged passages and indexed as dense embeddings. Retrieved passages were used to generate citation-grounded responses through a large language model (LLM). Open-weight, proprietary, and non-RAG baseline models across Gemini and GPT families were evaluated on easy, medium, and hard registry questions using an LLM-as-a-Judge protocols. RAG configurations consistently outperformed non-RAG approaches, especially as question difficulty increased. Mean grounding scores for RAG were 0.62/0.56/0.59 across easy/medium/hard tiers versus 0.29/0.26/0.29 for non-RAG. RAG models also achieved higher semantic-similarity scores overall. Proprietary RAG models performed strongest on easy and medium questions, while local RAG models ranked highest on hard questions and proprietary models were generally more cautious. Domain-specific RAG improved evidence grounding and response quality for cancer registry questions while enabling citation-supported assistance across complexity levels. CRISS demonstrates the potential of human-centered, citation-grounded AI to support cancer registrars while preserving human oversight for final coding decisions.
| Comments: | 21 pages, 13 figures, 7 tables. Keywords: cancer registry, retrieval-augmented generation, large language models, conversational AI, clinical informatics, oncology data specialists, medical question answering, AI safety, clinical decision support |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.29075 [cs.AI] |
| (or arXiv:2609.29075v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29075
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars, by Vani Seth and 6 other authors
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