更智能地聆听：人工智能在助听设备中应用的范围综述
Hearing Smarter: A Scoping Review of Artificial Intelligence Implementation in Hearing Devices.
文献信息
| PMID | 42941637 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Cameron Hanegan |
| 作者单位 | Department of Otolaryngology-Head and Neck Surgery, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA. |
| 期刊 | Journal of clinical medicine |
| SCI 分区 | Q1 |
| IF | 3.5 |
| 研究类型 | 综述 Meta · 临床 |
| 所属专科 | 耳科 |
中文摘要
背景/目的： 听力损失影响全球超过15.7亿人，许多本可从助听器获益的人并未佩戴助听器。噪声环境下言语理解不佳是主要主诉之一，已有多种人工智能（AI）模型被提出以解决该问题。本综述旨在刻画AI在助听设备中的应用特征，并提供从模型开发推进至临床应用的框架。
方法： 遵循PRISMA-ScR指南，检索PubMed/MEDLINE、Embase、CINAHL、Web of Science和APA PsycInfo，纳入2010年至2025年发表的同行评议原创研究。对每个模型收集AI任务、架构、输入、训练方法、验证和延迟等数据。
结果： 共46项研究符合纳入标准。语音增强是最常见的任务（58.7%），深度神经网络是最常见的架构（34.8%）。训练以离线（93.5%）和监督式（91.3%）为主。验证主要依赖数据库/模拟（71.7%）和听力正常听者（39.1%）；仅1个模型（2.2%）在真实世界条件下使用听力受损听者进行验证。10个模型（21.7%）满足助听器使用的延迟要求。
结论： AI已被应用于多种助听设备任务，但大多数模型仍处于开发早期。临床转化将需要在设备端实现，并在听力受损听者中进行真实世界验证。
英文摘要
Background/Objectives: Hearing loss affects over 1.57 billion people worldwide, and many who could benefit from hearing aids do not wear them. Poor speech understanding in noise is one of the main complaints, and several artificial intelligence (AI) models have been proposed to address it. This review aims to characterize AI applications in hearing devices and offers a framework for advancing from model development to clinical application. Methods: Following PRISMA-ScR guidelines, PubMed/MEDLINE, Embase, CINAHL, Web of Science, and APA PsycInfo were searched for peer-reviewed original studies published from 2010 to 2025. Data regarding AI task, architecture, input, training method, validation, and latency were collected for each model. Results: In total, 46 studies met the inclusion criteria. Speech enhancement was the most common task (58.7%), and deep neural networks were the most common architecture (34.8%). Training was predominantly offline (93.5%) and supervised (91.3%). Validation relied mainly on database/simulation (71.7%) and normal-hearing listeners (39.1%); only 1 model (2.2%) was validated using hearing-impaired listeners in real-world conditions. Ten models (21.7%) met the latency requirements for hearing aid use. Conclusions: AI has been applied across many hearing device tasks, but most models remain early in development. Clinical translation will require on-device implementation and real-world validation in hearing-impaired listeners.