{"ID":2898124,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.04026","arxiv_id":"2507.04026","title":"Patient-Centered RAG for Oncology Visit Aid Following the Ottawa Decision Guide","abstract":"Effective communication is essential in cancer care, yet patients often face challenges in preparing for complex medical visits. We present an interactive, Retrieval-augmented Generation-assisted system that helps patients progress from uninformed to visit-ready. Our system adapts the Ottawa Personal Decision Guide into a dynamic retrieval-augmented generation workflow, helping users bridge knowledge gaps, clarify personal values and generate useful questions for their upcoming visits. Focusing on localized prostate cancer, we conduct a user study with patients and a clinical expert. Results show high system usability (UMUX Mean = 6.0 out of 7), strong relevance of generated content (Mean = 6.7 out of 7), minimal need for edits, and high clinical faithfulness (Mean = 6.82 out of 7). This work demonstrates the potential of combining patient-centered design with language models to enhance clinical preparation in oncology care.","short_abstract":"Effective communication is essential in cancer care, yet patients often face challenges in preparing for complex medical visits. We present an interactive, Retrieval-augmented Generation-assisted system that helps patients progress from uninformed to visit-ready. Our system adapts the Ottawa Personal Decision Guide int...","url_abs":"https://arxiv.org/abs/2507.04026","url_pdf":"https://arxiv.org/pdf/2507.04026v1","authors":"[\"Siyang Liu\",\"Lawrence Chin-I An\",\"Rada Mihalcea\"]","published":"2025-07-05T12:37:26Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"RAG\",\"Language Model\"]","has_code":false}