(1) Burn injuries demand multidisciplinary, evidence-based care, yet the extensive literature complicates timely decision making. Retrieval-augmented generation (RAG) synthesizes research while addressing inaccuracies in pretrained models. However, citation bias in sourcing for RAG often prioritizes highly cited studies, overlooking less-cited but valuable research. This study examines RAG's performance in burn management, comparing citation levels to enhance evidence synthesis, reduce selection bias, and guide decisions. (2) Two burn management datasets were assembled: 30 highly cited (mean: 303) and 30 less-cited (mean: 21). The Gemini-1.0-Pro-002 RAG model addressed 30 questions, ranging from foundational principles to advanced surgical approaches. Responses were evaluated for accuracy (5-point scale), readability (Flesch-Kincaid metrics), and response time with Wilcoxon rank sum tests (p < 0.05). (3) RAG achieved comparable accuracy (4.6 vs. 4.2, p = 0.49), readability (Flesch Reading Ease: 42.8 vs. 46.5, p = 0.26; Grade Level: 9.9 vs. 9.5, p = 0.29), and response time (2.8 vs. 2.5 s, p = 0.39) for the highly and less-cited datasets. (4) Less-cited research performed similarly to highly cited sources. This equivalence broadens clinicians' access to novel, diverse insights without sacrificing quality. As plastic surgery evolves, RAG's inclusive approach fosters innovation, improves patient care, and reduces cognitive burden by integrating underutilized studies. Embracing RAG could propel the field toward dynamic, forward-thinking care.
Keywords: AI (artificial intelligence); RAG (retrieval-augmented generation); burn; clinical decision support; large language model; plastic surgery.