Epistemology and technology have been working in synergy throughout history. This relationship has culminated in large language models (LLMs). LLMs are rapidly becoming integral parts of our daily lives through smartphones and personal computers, and we are coming to accept the functionality of LLMs as a given. As LLMs become more entrenched in societal functioning, questions have begun to emerge: Are LLMs capable of real understanding? What is knowledge in LLMs? Can knowledge exist independently of a conscious observer? While these questions cannot be answered definitively, we can argue that modern LLMs are more than mere symbol-manipulators and that LLMs in deep neural networks should be considered capable of a form of knowledge, though it may not qualify as justified true belief (JTB) in the traditional definition. This deep neural network design may have endowed LLMs with the capacity for internal representations, basic reasoning, and the performance of seemingly cognitive tasks, possible only through a compressive but generative form of representation that can be best termed as knowledge. In addition, the non-symbolic nature of LLMs renders them incompatible with the criticism posed by Searle’s “Chinese room” argument. These insights encourage us to revisit fundamental questions of epistemology in the age of LLMs, which we believe can advance the field.

Study Design: Retrospective cohort study. Objective: This study aimed to determine how decompressive lumbar laminectomy (DLL) procedure volumes changed after the publication of major randomized controlled trials (RCTs) that investigated the clinical efficacy of DLL. Summary of Background Data: Two RCTs published in 2015 and one published in 2018 demonstrated no benefit of DLL compared with nonoperative care in intention-to-treat analyses. However, it remains unclear how these RCTs have influenced clinical practice. Methods: After conducting a systematic literature review to identify major DLL RCTs, we analyzed DLL volumes in an aggregated data set that contains Medicare Part B procedure data from 2013 to 2023. Results: Throughout 2013 to 2023, normalized DLL procedure volumes generally decreased. Before the 2015 RCTs, the slope of the normalized DLL procedure volumes was −0.22, which then decreased to −0.26 after 2015 ( P <0.0001). Before the 2018 publication date of the RCT from Abdu and colleagues, the slope of the DLL procedure volumes was −0.63, which then increased to −0.04 after 2018 ( P <0.0001). Conclusions: Normalized DLL procedure volumes generally decreased from 2013 to 2023, but the rate of decline accelerated after the publication of two RCTs in 2015 and decelerated after the publication of the 2018 RCT. Therefore, although clinical practice trends aligned directionally with the evidence, the magnitude and consistency of response to high-level evidence varied over time. Level of Evidence: Level IV.