Clinical Translation of Artificial Intelligence-Driven Gait Analysis Using Plantar Pressure and Ground Reaction Force

Background: Artificial intelligence (AI)-driven gait analysis using plantar pressure and ground reaction force (GRF) signals may provide objective digital biomarkers for rehabilitation, but clinical translation remains uncertain. This scoping review and evidence map aimed to summarize clinical applications, compare evidence maturity, and identify methodological and translational gaps. Methods: PubMed, Web of Science, Embase, and Scopus were searched from the earliest available indexed records in each database to May 2026. Original clinical studies using plantar pressure- or GRF-derived signals with AI methods for disease recognition, severity assessment, risk prediction, rehabilitation monitoring, or decision support were included. Results: Fifteen studies met the eligibility criteria. Evidence was concentrated in Parkinson's disease (PD), particularly PD recognition and freezing of gait prediction, where relatively more mature evidence was supported by multiple studies and participant-level or cross-dataset validation. Evidence for PD severity assessment, knee osteoarthritis monitoring, chronic ankle instability rehabilitation, fall-risk stratification, sarcopenia screening, peripheral artery disease recognition, and functional gait disorder classification remained less mature. Translation was limited by small or single-center samples, unclear participant-level data splitting, limited external validation, absent calibration, sparse explainable AI reporting, and insufficient real-world workflow testing. Conclusions: Future studies should prioritize prospective, externally validated, interpretable, calibrated, and clinically embedded models before routine rehabilitation implementation.