Model reduction techniques and data-driven modeling, powered by machine learning and artificial intelligence, have advanced to a stage where they can effectively address complex challenges in remote fields such as medicine. The ability to make real-time predictions for patient assessments and predict treatment outcomes has drawn many practitioners to explore machine learning and model order reduction to enhance patient care and improve intervention [1,2].

This minisymposium aims to showcase the latest advancements in the use of machine learning and model reduction techniques within medical and biomedical applications. It will also address novel computational methods developed to tackle the unique challenges of modeling and simulating medical and biomedical phenomena. Topics of interest include, but are not limited to, patient-specific modeling and simulation, the application of model reduction techniques in the medical field, and the use of artificial intelligence in medical assessment and prognosis.