mlsanityData to 7 Oct 2026

Patient-Centered RAG for Oncology Visit Aid Following the Ottawa Decision Guide

arXiv 2507.04026 ↗First posted 5 Jul 2025cs.CLcited by 0

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 1 of its 17 references that are in the map. First posted on arXiv 5 Jul 2025.

Builds on

1 of 17 references.

References · 1

  1. 2025PreCare: Designing AI Assistants for Advance Care Planning (ACP) to Enhance Personal Value Exploration, Patient Knowledge, and Decisional Confidencecited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  2. 2024Query pipeline optimization for cancer patient question answering systems
  3. 2025Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
  4. 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights
  5. 2026CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars
  6. 2025Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines
  7. 2025CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records
  8. 2024Onco-Retriever: Generative Classifier for Retrieval of EHR Records in Oncology