Artificial Intelligence for Patient Support: Assessing Retrieval-Augmented Generation for Answering Postoperative Rhinoplasty Questions
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View abstract on PubMed
Summary
This summary is machine-generated.Retrieval-Augmented Generation (RAG) shows promise for AI in plastic surgery, but challenges remain in accuracy and patient understanding. Further improvements are needed for safe clinical use of these AI models.
Area Of Science
- Medical Informatics
- Artificial Intelligence in Healthcare
- Plastic Surgery
Background
- Large language models (LLMs) in healthcare risk patient safety due to inaccurate information.
- Retrieval-Augmented Generation (RAG) enhances AI reliability using curated knowledge bases.
- RAG is particularly beneficial for high-demand medical specialties like plastic surgery.
Purpose Of The Study
- To evaluate Retrieval-Augmented Generation (RAG) AI models for postoperative rhinoplasty patient inquiries.
- To assess the safety and identify areas for improvement in RAG model implementation for clinical use.
Main Methods
- Four RAG models (Gemini-1.0-Pro-002, Gemini-1.5-Flash-001, Gemini-1.5-Pro-001, PaLM 2) were tested.
- Models answered 30 common patient questions using authoritative rhinoplasty texts.
- Responses were evaluated for accuracy, comprehensiveness, readability, and actionability using established metrics and statistical analysis.
Main Results
- Responses were generally accurate (41.7% fully accurate), but a significant nonresponse rate (30.8%) was observed.
- Gemini-1.0-Pro-002 showed superior comprehensiveness, but readability and understandability were below patient education standards.
- PaLM 2 performed lowest in response actionability.
Conclusions
- RAG application in rhinoplasty care demonstrates potential for accuracy but faces limitations in nonresponse and contextual understanding.
- Addressing these challenges is crucial for safe and effective RAG implementation in diverse medical fields.
- RAG models can potentially transform patient care by reducing physician workload and improving patient engagement.
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