
Large Language Models (LLMs) have shown remarkable capabilities in processing and generating human-like text, which has made them valuable tools in various fields, including healthcare. However, the reliance on vast amounts of training data renders these models susceptible to data-poisoning. According to the study, introducing just 0.001% of incorrect medical information into the training data can lead to erroneous outputs










