AI-Driven Horizons; Transforming Assistive Technology for Inclusive Healthcare: An opinion Article

Abstract

Background: Assistive technology (AT) has grown from a specialized field into a major source of mainstream innovation, while also benefiting from rapid advances in everyday consumer technology. Artificial Intelligence (AI) refers to computer systems capable of tasks requiring human intelligence, such as speech recognition, visual processing, and object identification. Forms of AI relevant to AT include generative AI, transfer learning, human-in-the-loop AI, and embodied AI. AI is integral in developing adaptive aid, autonomous wheelchairs, guidance systems, facial recognition tools, and smart home platforms, facilitating communication, mobility, and independence. Objective: This opinion article examines the role of artificial intelligence in assistive technology, exploring how AI-driven innovations can optimize health outcomes for individuals with functional impairments by enhancing accuracy, personalization, and scalability of assistive solutions, while addressing inherent limitations and challenges specific to AI applications in this field. Key Points: AI-powered AT systems offer extraordinary accuracy, personalization, and scalability through machine learning algorithms, speech recognition, smart prosthetics, and autonomous guidance systems. However, these systems face challenges including data bias, limited generalizability, privacy concerns, and short real-world evaluation. Despite these challenges, AI continues to transform assistive healthcare by enabling more adaptive, responsive, and user-centered technologies that promote greater autonomy and quality of life for people with disabilities. Continued advancements and rigorous real-world testing are imperative to address these limitations and fully realize AI’s potential in assistive technology. Additionally, the validation of these systems through more extensive and long-term real-world testing is crucial to ensure consistent safety and effectiveness. Current AT development remains in its infancy, with persistent limitations in speech recognition for dysarthria and safety detection in smart wheelchairs. Conclusion: AI-powered assistive technology (AT) offers remarkable accuracy, personalization, and scalability through machine learning, speech recognition, smart prosthetics, and autonomous navigation. These systems significantly improve independence and quality of life for individual with functional impairments by adapting to individual needs. While ongoing challenges include addressing data bias, enhancing model generalizability, ensuring privacy, and validating long-term safety, continued innovation is driving rapid progress. Limitations remain in areas like speech recognition for dysarthria and hazard detection in smart wheelchairs, but these spur further research and refinement, promising smarter, more responsive, and accessible AT solutions in the near future.