Generative AI for Web Engineering Models in Physical Therapy

Abstract

This study explores the integration of generative artificial intelligence (AI) into web- grounded physical remedy operations through a mixed- styles approach, combining both quantitative and qualitative data. Aquasi-experimental design was employed to compare the efficacity of AI- enhanced remedy with traditional styles, revealing significant advancements in remedy issues. Generative inimical Networks (GANs) and Large Language Models (LLMs) were employed to produce individualized exercise routines and grease interactive case- AI relations. Quantitative results showed that cases using AI-enhanced remedy endured lesser advancements in range of stir and advanced satisfaction compared to those witnessing traditional remedy. The study also linked several specialized and ethical challenges, including data sequestration enterprises and model limitations. Unborn exploration should concentrate on advancing AI algorithms, integrating multimodal feedback, and addressing usability issues to enhance the effectiveness and availability of AI in physical remedy.