Evidence-based artificial intelligence: Implementing retrieval-augmented generation models to enhance clinical decision support in plastic surgery

Abstract

The rapid advancement of large language models (LLMs) has generated significant enthusiasm within healthcare, especially in supporting clinical decision-making and patient management. However, inherent limitations including hallucinations, outdated clinical context, and unreliable references pose serious concerns for their clinical utility. Retrieval-Augmented Generation (RAG) models address these limitations by integrating validated, curated medical literature directly into AI workflows, significantly enhancing the accuracy, relevance, and transparency of generated outputs. This viewpoint discusses how RAG frameworks can specifically benefit plastic and reconstructive surgery by providing contextually accurate, evidence-based, and clinically grounded support for decision-making. Potential clinical applications include clinical decision support, efficient evidence synthesis, customizable patient education, informed consent materials, multilingual capabilities, and structured surgical documentation. By querying specialized databases that incorporate contemporary guidelines and literature, RAG models can markedly reduce inaccuracies and increase the reliability of AI-generated responses. However, the implementation of RAG technology demands rigorous database curation, regular updating with guidelines from surgical societies, and ongoing validation to maintain clinical relevance. Addressing challenges related to data privacy, governance, ethical considerations, and user training remains critical for successful clinical adoption. In conclusion, RAG models represent a significant advancement in overcoming traditional LLM limitations, promoting transparency and clinical accuracy with great potential for plastic surgery. Plastic surgeons and researchers are encouraged to explore and integrate these innovative generative AI frameworks to enhance patient care, surgical outcomes, communication, documentation quality, and education.

Keywords: Artificial intelligence; Clinical decision support; Large language models; Retrieval-augmented generation models.