<p class="jove_content">Healthcare-specific large language models face ethical and technical challenges, as they, like other large language models, reflect the biases inherent in their training data. The protocol presented here verifies the suppression of four types of bias (gender, race, occupation, religion) using prompt variants across three open-source models.</p>

enhancing-fairness-large-language-models-for-clinical-artificial

Institution - Delhi Technological University

Articles Published in JoVE