AI语音在医疗中存在包容性短板，可能加剧健康不平等。
Inclusivity of AI Speech in Healthcare: A Decade Look Back
- 聚焦医疗AI语音数据集的偏见问题，揭示资源语言与特定群体主导现象。
- 指出当前研究严重偏向高资源语言与标准口音，忽视边缘化群体。
- 呼吁构建包容性数据集与政策框架，保障医疗AI公平可用。
AI语音识别技术融入医疗领域有望重塑临床流程与医患沟通。然而本研究揭示显著的包容性缺口：数据集和研究过度偏重高资源语言、标准口音及狭窄人口群体。此类偏见可能导致AI系统对边缘群体语音识别失误，进而延续甚至加剧健康不平等。本文强调亟需推动包容性数据集设计、偏差缓解研究以及政策框架建设，确保医疗AI语音技术的公平可及。
原文摘要 · Abstract (English)
The integration of AI speech recognition technologies into healthcare has the potential to revolutionize clinical workflows and patient-provider communication. However, this study reveals significant gaps in inclusivity, with datasets and research disproportionately favouring high-resource languages, standardized accents, and narrow demographic groups. These biases risk perpetuating healthcare disparities, as AI systems may misinterpret speech from marginalized groups. This paper highlights the urgent need for inclusive dataset design, bias mitigation research, and policy frameworks to ensure equitable access to AI speech technologies in healthcare.
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