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连接神经生物学与人工智能:关于人工耳蜗和听觉神经假体听力恢复进展的综述之综述

Bridging Neurobiology and Artificial Intelligence: A Narrative Review of Reviews on Advances in Cochlear and Auditory Neuroprostheses for Hearing Restoration.

作者信息

Giansanti Daniele

机构信息

Istituto Superiore di Sanità, Via Regina Elena 299, 00161 Rome, Italy.

出版信息

Biology (Basel). 2025 Sep 22;14(9):1309. doi: 10.3390/biology14091309.

Abstract

BACKGROUND

Hearing loss results from diverse biological insults along the auditory pathway, including sensory hair cell death, neural degeneration, and central auditory processing deficits. Implantable auditory neuroprostheses, such as cochlear and brainstem implants, aim to restore hearing by directly stimulating neural structures. Advances in neurobiology and device technology underpin the development of more sophisticated implants tailored to the biological complexity of auditory dysfunction.

AIM

This narrative review of reviews aims to map the integration of artificial intelligence (AI) in auditory neuroprosthetics, analyzing recent research trends, key thematic areas, and the opportunities and challenges of AI-enhanced devices. By synthesizing biological and computational perspectives, it seeks to guide future interdisciplinary efforts toward more adaptive and biologically informed hearing restoration solutions.

METHODS

This narrative review analyzed recent literature reviews from PubMed and Scopus (last 5 years), focusing on AI integration with auditory neuroprosthetics and related biological processes. Emphasis was placed on studies linking AI innovations to neural plasticity and device-nerve interactions, excluding purely computational works. The ANDJ (a standard narrative review checklist) checklist guided a transparent, rigorous narrative approach suited to this interdisciplinary, rapidly evolving field.

RESULTS AND DISCUSSION

Eighteen recent review articles were analyzed, highlighting significant advancements in the integration of artificial intelligence with auditory neuroprosthetics, particularly cochlear implants. Established areas include predictive modeling, biologically inspired signal processing, and AI-assisted surgical planning, while emerging fields such as multisensory augmentation and remote care remain underexplored. Key limitations involve fragmented biological datasets, lack of standardized biomarkers, and regulatory challenges related to algorithm transparency and clinical application. This review emphasizes the urgent need for AI frameworks that deeply integrate biological and clinical insights, expanding focus beyond cochlear implants to other neuroprosthetic devices. To complement this overview, a targeted analysis of recent cutting-edge studies was also conducted, starting from the emerging gaps to capture the latest technological and biological innovations shaping the field. These findings guide future research toward more biologically meaningful, ethical, and clinically impactful solutions.

CONCLUSIONS

This narrative review highlights progress in integrating AI with auditory neuroprosthetics, emphasizing the importance of biological foundations and interdisciplinary approaches. It also recognizes ongoing challenges such as data limitations and the need for clear ethical frameworks. Collaboration across fields is vital to foster innovation and improve patient care.

摘要

背景

听力损失是由听觉通路中多种生物学损伤导致的,包括感觉毛细胞死亡、神经退变和中枢听觉处理缺陷。可植入式听觉神经假体,如人工耳蜗和脑干植入物,旨在通过直接刺激神经结构来恢复听力。神经生物学和设备技术的进步为开发更复杂的、适应听觉功能障碍生物复杂性的植入物奠定了基础。

目的

本综述旨在梳理人工智能(AI)在听觉神经假体中的整合情况,分析近期研究趋势、关键主题领域以及人工智能增强设备的机遇和挑战。通过综合生物学和计算视角,旨在指导未来跨学科努力,以实现更具适应性和生物学依据的听力恢复解决方案。

方法

本综述分析了来自PubMed和Scopus(过去5年)的近期文献综述,重点关注人工智能与听觉神经假体及相关生物学过程的整合。重点关注将人工智能创新与神经可塑性和设备 - 神经相互作用联系起来的研究,不包括纯计算工作。ANDJ(标准叙述性综述清单)清单指导了一种透明、严谨的叙述方法,适用于这个跨学科、快速发展的领域。

结果与讨论

分析了18篇近期综述文章,突出了人工智能与听觉神经假体,特别是人工耳蜗整合方面的重大进展。既定领域包括预测建模、受生物学启发的信号处理和人工智能辅助手术规划,而多感官增强和远程护理等新兴领域仍未得到充分探索。关键限制包括生物数据集分散、缺乏标准化生物标志物以及与算法透明度和临床应用相关的监管挑战。本综述强调迫切需要深度整合生物学和临床见解的人工智能框架,将关注重点从人工耳蜗扩展到其他神经假体设备。为补充此综述,还从新兴差距出发,对近期前沿研究进行了针对性分析,以捕捉塑造该领域的最新技术和生物学创新。这些发现指导未来研究朝着更具生物学意义、符合伦理且具有临床影响力的解决方案发展。

结论

本叙述性综述突出了人工智能与听觉神经假体整合方面的进展,强调了生物学基础和跨学科方法的重要性。它还认识到数据限制等持续存在的挑战以及明确伦理框架的必要性。跨领域合作对于促进创新和改善患者护理至关重要。

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