For machine learning to make a difference in healthcare, clinicians need to be able to count on it. This forum brings together two talks on building AI that holds up in real clinical settings. Aarat Satsangi will present an approach to detecting stroke on CT scans that combines multiple models into a team, shows which parts of the image drive each prediction, and measures how much the models agree, so that results are accurate and transparent. Daya Kumar will introduce NeurOCNN, a model that analyzes physiological signals such as EEG as continuous curves, allowing it to stay accurate when data comes from devices with different recording speeds, with no retraining needed. Together, the talks show how reliability, interpretability, and adaptability can move machine learning closer to clinical use, from medical imaging to wearable sensing.