Gait assessment plays a critical role in the diagnosis and management of neurological disorders. Although AI-based models offer the potential to enhance the objectivity and accessibility of clinical gait analysis, most existing models are limited by their dependence on specific patient populations and sensor configurations, largely due to the scarcity of diverse clinical datasets. In a recent study, researchers from IBM Research, Cleveland Clinic, and University of Tsukuba addressed this limitation by developing a novel framework that integrates synthetic gait data generated by generative AI with physics-based musculoskeletal simulation. This approach supports the development of scalable and generalizable gait analysis models applicable across heterogeneous patient groups and sensor environments.