Machine learning (ML) methods often achieve superhuman performance levels, however, most existing machine learning research in the medical domain is stalled at the research paper level and is not implemented into daily clinical practice. To achieve the overarching goal of realizing the promise of cutting-edge ML techniques and bring this exciting research to fruition, we must bridge the gap between research and clinics. In this workshop, we aim to bring together ML researchers and clinicians to discuss the challenges and potential solutions on how to enable the use of state-of-the-art ML techniques in the daily clinical practice and ultimately improve healthcare by trying to answer questions like: what are the procedures that bring humans-in-the-loop for auditing ML systems for healthcare? Are the proposed ML methods robust to changes in population, distribution shifts, or other types of biases? What should the ML methods/systems fulfill to successfully deploy them in the clinics? What are failure modes of ML models for healthcare? How can we develop methods for improved interpretability of ML predictions in the context of healthcare? And many others. We will further discuss translational and implementational aspects and talk about challenges and lessons learned from integrating an ML system into clinical workflow.