Radiologists play a central role in image annotation for training Machine Learning models. Key challenges in this regard include low inter-reader agreement for challenging cases and concerns of interpersonal bias amongst trainers. Inspired by biological swarm intelligence, we explored the use of real time consensus labeling by three sub-specialty (MSK) trained radiologists and five radiology residents in improving training data. A second swarm session with three residents was conducted to explore the effect of swarm size. These results were validated against clinical ground truth and also compared with results from a state-of-the-art AI model tested on the same dataset.