Patient-Centered RAG for Oncology Visit Aid Following the Ottawa Decision Guide
Liu, Siyang, An, Lawrence Chin-I, Mihalcea, Rada
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Jul-8-2025
- Country:
- Africa (0.04)
- North America > United States
- Maryland > Montgomery County
- Bethesda (0.04)
- Michigan > Washtenaw County
- Ann Arbor (0.05)
- Maryland > Montgomery County
- Genre:
- Questionnaire & Opinion Survey (1.00)
- Research Report > New Finding (0.89)
- Industry:
- Health & Medicine > Therapeutic Area > Oncology (1.00)
- Technology: