# The Lyme Disease Controversy: An AI-Driven Discourse Analysis of a Quarter Century of Academic Debate and Divides

arXiv: https://arxiv.org/abs/2504.08777 · HTML: https://mlsanity.com/paper/2504.08777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 130 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [Can large language models replace humans in the systematic review process? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages](https://mlsanity.com/paper/2310.17526/) (2023)
- [Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature](https://mlsanity.com/paper/2302.06474/) (2023)

## Abstract (arXiv)

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.