  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 agonists
(GLP-1 RA), a market expected to grow exponentially to $133.5 billion USD by
2030. Using a Named Entity Recognition (NER) model, our method successfully
detected 21 potential ASEs overlooked upon FDA approval, including irritability
and numbness. Our data-analytic approach revolutionizes the detection of
unreported ASEs associated with newly deployed drugs, leveraging cutting-edge
AI-driven social media analytics. It can increase the safety of new drugs in
the marketplace by unlocking the power of social media to support regulators
and manufacturers in the rapid discovery of hidden ASE risks.
