Artificial Intelligence for Patient Support: Assessing Retrieval-Augmented Generation for Answering Postoperative Rhinoplasty Questions
This first application of RAG to postoperative rhinoplasty patient care highlights its strengths in accuracy alongside its limitations, including nonresponse and contextual understanding. Addressing these challenges will enable safer, more effective implementation of RAG models across diverse surgical and medical contexts, with the potential to revolutionize patient care by reducing physician workload while enhancing patient engagement.
Authors
- Sahar Borna (ORCID: https://orcid.org/0000-0002-7845-7356)
- Keith T. Aziz (ORCID: https://orcid.org/0000-0003-4034-1750)
- Antonio J. Forte (ORCID: https://orcid.org/0000-0003-2004-7538)
- Cui Tao (ORCID: https://orcid.org/0000-0002-4267-1924)
- Cesar A. Gomez-Cabello (ORCID: https://orcid.org/0009-0008-0603-3192)
- Syed Ali Haider (ORCID: https://orcid.org/0009-0007-5621-2861)
- Peter M. Murray (ORCID: https://orcid.org/0000-0003-1405-204X)
- Ariana Genovese (ORCID: https://orcid.org/0009-0000-9678-2163)
- Srinivasagam Prabha (ORCID: https://orcid.org/0000-0003-2573-8493)
- Maissa Trabilsy
Institutions
- WinnMed (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Aesthetic Surgery Journal
- Published
- 2025-03-15
- DOI
- https://doi.org/10.1093/asj/sjaf038
- Citations
- 13
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 8.51