Inclusivity of AI Speech in Healthcare: A Decade Look Back

TL;DR

This paper reviews a decade of AI speech in healthcare, highlighting significant inclusivity gaps and emphasizing the need for diverse datasets and policies to prevent disparities.

Contribution

It provides a comprehensive analysis of biases in AI speech datasets and calls for inclusive practices and policies to improve healthcare equity.

Findings

Datasets favor high-resource languages and standard accents.

Biases in AI speech systems can worsen healthcare disparities.

Urgent need for inclusive dataset design and bias mitigation.

Abstract

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.