BACKGROUND AND OBJECTIVES: Scholarly output is accelerating in medical domains, making it challenging to keep up with the latest neurosurgical literature. The emergence of large language models (LLMs) has facilitated rapid, high-quality text summarization. However, LLMs cannot autonomously conduct literature reviews and are prone to hallucinating source material. We devised a novel strategy that combines Reference Publication Year Spectroscopy—a bibliometric technique for identifying foundational articles within a corpus—with LLMs to automatically summarize and cite salient details from articles. We demonstrate our approach for four common spinal conditions in a proof of concept. METHODS: Reference Publication Year Spectroscopy identified seminal articles from the corpora of literature for cervical myelopathy, lumbar radiculopathy, lumbar stenosis, and adjacent segment disease. The article text was split into 1024-token chunks. Queries from three knowledge domains (surgical management, pathophysiology, and natural history) were constructed. The most relevant article chunks for each query were retrieved from a vector database using chain-of-thought prompting. LLMs automatically summarized the literature into a comprehensive narrative with fully referenced facts and statistics. Information was verified through manual review, and spine surgery faculty were surveyed for qualitative feedback. RESULTS: Our tandem approach cost less than $1 for each condition and ran within 5 minutes. Generative Pre-trained Transformer–4 was the best-performing model, with a near-perfect 97.5% citation accuracy. Surveys of spine faculty helped refine the prompting scheme to improve the cohesion and accessibility summaries. The final artificial intelligence–generated text provided high-fidelity summaries of each pathology's most clinically relevant information. CONCLUSION: We demonstrate the rapid, automated summarization of seminal articles for four common spinal pathologies, with a generalizable workflow implemented using consumer-grade hardware. Our tandem strategy fuses bibliometrics and artificial intelligence to bridge the gap toward fully automated knowledge distillation, obviating the need for manual literature review and article selection.
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