Utilizing AI and Social Media Analytics to Discover Unreported Adverse Side Effects of GLP-1 Receptor Agonists Used for Obesity Treatment

Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. To promptly detect overlooked ASEs, we developed a digital health methodology capable of analyzing massive public data from social media, published clinical research, manufacturers' reports, and ChatGPT. We uncovered ASEs associated with the glucagon-like peptide 1 receptor agonist (GLP-1 RA) medications used to treat diabetes and obesity, a market expected to grow exponentially to $133.5 billion USD by 2030. Using a named entity recognition model, our method successfully detected 15 potential ASEs of GLP-1 RAs, overlooked upon FDA approval. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed medications, leveraging cutting-edge AI-driven social media analytics. This ongoing research can increase the safety of new medications in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks.