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Background <p>This narrative study explores the technological incorporation of AI into upper-limb prosthetics, focusing on the move from classical myoelectric control to autonomous sensor-fused architectures. The study’s goal is to combine the most recent technology developments in machine learning, edge computing, and energy management to create a path to the “technological frontier” of bionic rehabilitation.</p> Methods <p>From the general pool of databases, a systematic search of PubMed, IEEE Xplore, Scopus, and Google Scholar yielded peer-reviewed literature published between 2013 and 2024. Following the protocol of quality over quantity filtration (PRISMA 2020), thirty of the major studies were identified based on hardware validation and clinical outcome reporting criteria.</p> Results <p>A comparative analysis would show that AI-driven architectures, namely, Pattern Recognition (PR) and Regression based control, would have improved the success rates from 75% against legacy systems to more than 92%. Current state-of-the-art devices, like the Taska Hand and Hero Arm, demonstrate end-to-end control latencies below the human perception threshold of 125 ms by utilizing Edge AI, e.g., ARM Cortex-M7 processors. Moreover, the addition of computer vision linked to open control loops reduces user cognitive load and compensatory shoulder movements by 30%. Emerging energy-harvesting strategies exhibit peak power outputs of 25.8 mW, suggesting an increase in battery longevity by 15.7%.</p> Conclusion <p>AI has transformed the deterministic mechanical tools into stochastic intuitive bionic extensions. However, they still remain high-cost mechanical gadgets represented at the end-user level, cause thermal management problems, and have battery density issues. The future research will have to focus on bringing together soft robotics, 3D printable customization, and non-invasive neural interfaces to make these high-performance systems accessible and sustainable to a worldwide amputee population.</p>

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A narrative review on advances in upper limb prosthetics empowered by artificial intelligence highlights a technical revolution in integrated control and functionality for the user

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Nadia Hachoumi,
  • Rquia Laabidi,
  • Mohamed Eddabbah

摘要

Background

This narrative study explores the technological incorporation of AI into upper-limb prosthetics, focusing on the move from classical myoelectric control to autonomous sensor-fused architectures. The study’s goal is to combine the most recent technology developments in machine learning, edge computing, and energy management to create a path to the “technological frontier” of bionic rehabilitation.

Methods

From the general pool of databases, a systematic search of PubMed, IEEE Xplore, Scopus, and Google Scholar yielded peer-reviewed literature published between 2013 and 2024. Following the protocol of quality over quantity filtration (PRISMA 2020), thirty of the major studies were identified based on hardware validation and clinical outcome reporting criteria.

Results

A comparative analysis would show that AI-driven architectures, namely, Pattern Recognition (PR) and Regression based control, would have improved the success rates from 75% against legacy systems to more than 92%. Current state-of-the-art devices, like the Taska Hand and Hero Arm, demonstrate end-to-end control latencies below the human perception threshold of 125 ms by utilizing Edge AI, e.g., ARM Cortex-M7 processors. Moreover, the addition of computer vision linked to open control loops reduces user cognitive load and compensatory shoulder movements by 30%. Emerging energy-harvesting strategies exhibit peak power outputs of 25.8 mW, suggesting an increase in battery longevity by 15.7%.

Conclusion

AI has transformed the deterministic mechanical tools into stochastic intuitive bionic extensions. However, they still remain high-cost mechanical gadgets represented at the end-user level, cause thermal management problems, and have battery density issues. The future research will have to focus on bringing together soft robotics, 3D printable customization, and non-invasive neural interfaces to make these high-performance systems accessible and sustainable to a worldwide amputee population.