Dr. Dante Trabassi is a biomedical engineer and PhD candidate at Sapienza University in Rome who studies Artificial Intelligence in biomechanics, movement disorders, and clinical decision-making. His research focuses on combining machine learning, deep learning, and generative AI with sensor-based data (IMUs, motion capture, EMG) to improve diagnostics and rehabilitation in neuromuscular and neurodegenerative diseases. Dr. Trabassi has extensive experience in gait analysis, data fusion techniques, and explainable AI (XAI), which ensure model interpretability for clinical applications. He has worked with multidisciplinary teams, including neurologists, orthopedists, and physiotherapists, on projects ranging from fall risk prediction to creating synthetic data for rare disease classification. Dr. Trabassi has also studied SHAP-based explainability, recurrence quantification analysis (RQA), and entropy-based metrics in movement analysis. As a lecturer in AI applications in healthcare, he seeks to bridge the gap between technical research and clinical practice. His work contributes to improving patient outcomes through personalized medicine, real-time monitoring systems, and AI-driven decision support in healthcare. He is actively involved in scientific dissemination, contributing to conferences, peer-reviewed journals, and collaborative research projects.