Inclusivity of AI Speech in Healthcare: A Decade Look Back
This addresses the problem of AI bias in healthcare for marginalized groups, but it is incremental as it highlights known issues without new solutions.
The study identified significant inclusivity gaps in AI speech recognition for healthcare, showing datasets and research disproportionately favor high-resource languages and narrow demographics, which risks perpetuating healthcare disparities.
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.