The Lyme Disease Controversy: An AI-Driven Discourse Analysis of a Quarter Century of Academic Debate and Divides
This provides a scalable methodology for discourse analysis in contested medical fields, though it is incremental as it applies existing AI techniques to a new domain.
This study tackled the problem of analyzing the polarized academic debate on Chronic Lyme Disease and Post-Treatment Lyme Disease Syndrome over 25 years by developing a hybrid AI-driven methodology combining large language models with human validation to analyze thousands of scholarly abstracts, revealing a shift from infection-based to immune-mediated explanations for persistent symptoms.
The scientific discourse surrounding Chronic Lyme Disease (CLD) and Post-Treatment Lyme Disease Syndrome (PTLDS) has evolved over the past twenty-five years into a complex and polarised debate, shaped by shifting research priorities, institutional influences, and competing explanatory models. This study presents the first large-scale, systematic examination of this discourse using an innovative hybrid AI-driven methodology, combining large language models with structured human validation to analyse thousands of scholarly abstracts spanning 25 years. By integrating Large Language Models (LLMs) with expert oversight, we developed a quantitative framework for tracking epistemic shifts in contested medical fields, with applications to other content analysis domains. Our analysis revealed a progressive transition from infection-based models of Lyme disease to immune-mediated explanations for persistent symptoms. This study offers new empirical insights into the structural and epistemic forces shaping Lyme disease research, providing a scalable and replicable methodology for analysing discourse, while underscoring the value of AI-assisted methodologies in social science and medical research.