Transformer-Based Misinformation Detection in Headache Medicine: A BERT Model Approach.

The widespread dissemination of misinformation in headache medicine through digital platforms, including social media and blogs, poses significant challenges for both clinicians and patients. Erroneous information about headache treatments can lead to misinformed clinical decisions, patient anxiety, and suboptimal therapeutic outcomes. To mitigate this issue, we propose a Bidirectional Encoder Representations from Transformers (BERT)-based artificial intelligence (AI) model for automated classification of true versus misleading headache-related information. Our model was trained on a domain-specific dataset comprising 1,950 expert-validated statements derived from the International Classification of Headache Disorders 3 (ICHD-3). Utilizing pre-trained BERT embeddings fine-tuned on this dataset, our model achieved a classification accuracy of 97.43% on an independent test set, outperforming traditional machine learning baselines. These results underscore the efficacy of transformer-based architectures in combating misinformation and demonstrate their potential integration into clinical decision-support systems to enhance medical information reliability.Clinical Relevance- Using a BERT-based AI model, we are able to classify true versus misinformation within headache content databases. This tool provides a valuable resource for both clinicians and patients, enabling them to discern credible information and make informed decisions regarding headache treatment options.