In the field of machine learning for medical imaging, rigorous experimentation is essential to develop accurate and reliable models that can significantly impact patient outcomes. These experiments involve testing various datasets and machine learning models to achieve high accuracy levels before deploying the models in real-world medical applications. Efficient management of these experiments is crucial, as the process involves handling numerous datasets, annotators, and experiment results, which can be challenging and time-consuming. High-quality and diverse data are vital for successful outcomes, and failures should be viewed as learning opportunities to refine hypotheses and improve models. Effective workflows, including the use of automated annotation tools, can enhance the efficiency of these experiments, ensuring robust and unbiased models that meet stringent healthcare standards. Encord offers an automated image annotation platform developed in collaboration with medical professionals to streamline the annotation process and improve data quality, helping teams to achieve better experiment efficiency and quicker deployment of machine learning models in healthcare settings.