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Evidence-based artificial intelligence: Implementing retrieval-augmented generation models to enhance clinical
1Department of Plastic Surgery, Cleveland Clinic, Cleveland, OH, USA.
Summary
Retrieval-Augmented Generation (RAG) models enhance artificial intelligence for plastic surgery by integrating validated medical literature. This improves accuracy and reliability in clinical decision-making and patient care.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Surgery
- Plastic and Reconstructive Surgery
Background:
- Large language models (LLMs) show promise in healthcare but suffer from inaccuracies like hallucinations and outdated information.
- These limitations hinder the clinical utility of LLMs in patient management and decision-making.
Purpose of the Study:
- To discuss the benefits of Retrieval-Augmented Generation (RAG) frameworks in plastic and reconstructive surgery.
- To highlight RAG's potential in providing accurate, evidence-based clinical support.
Main Methods:
- Integrating validated, curated medical literature into AI workflows.
- Querying specialized databases with contemporary guidelines and literature.
Main Results:
- RAG models enhance accuracy, relevance, and transparency of AI-generated outputs.
- Potential applications include clinical decision support, evidence synthesis, patient education, and documentation.
Conclusions:
- RAG models significantly improve upon traditional LLMs for plastic surgery by ensuring clinical accuracy and reliability.
- Successful implementation requires rigorous database curation, regular updates, and validation, alongside addressing ethical and training challenges.
