Artificial intelligence in physical therapy refers to algorithmic and computational methods used in patient assessment, treatment planning, clinical documentation, robotic rehabilitation, and remote monitoring. Adoption varies across healthcare settings, and most systems function as decision-support tools rather than replacements for clinical judgement.
History
Research related to artificial intelligence in physical therapy developed from earlier work in rehabilitation robotics and computerized movement analysis. Robotic devices were evaluated in clinical settings beginning in the late 1980s for repetitive upper- and lower-limb movements. Additional platforms for gait and arm training were tested during the 1990s.
Computer-assisted movement analysis expanded during the same period through the use of motion-capture systems, sensor arrays, and virtual-environment interfaces. These systems produced datasets used to develop algorithms for movement classification. By the 2000s, machine-learning models were applied to sensor and video data to identify movement patterns and evaluate exercise technique.
Deep-learning methods
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Source: Wikipedia
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