Advancing Health Equity through AI-Powered Medical Imaging: A Case Study of HealthAI and the Challenges of Accessibility and Bias

Author: Dev R Gupta

Affiliation: Horace Mann

Abstract:

This paper explores how artificial intelligence, specifically through the application HealthAI, can be used to address healthcare disparities and advance health equity in medical imaging. The study begins with a literature review focused on diagnostic accuracy, implementation challenges, digital divide issues, and ethical concerns such as bias and data representation. HealthAI is then presented as a case study to show how an AI-powered diagnostic tool can bridge gaps in access to care, especially in underserved regions where radiologist shortages are common. The research analyzes HealthAI’s design, accuracy, and implementation through the lens of health equity frameworks from the World Health Organization. Results show that while HealthAI demonstrates high diagnostic accuracy and ease of use, challenges remain around regulatory approval, digital literacy, and ensuring representative data. The discussion connects these findings to broader themes in the literature, emphasizing the need for transparency, inclusivity, and ongoing evaluation to prevent AI from reinforcing existing disparities. The paper concludes that AI has significant potential to make healthcare more equitable, but this depends on careful attention to accessibility, bias mitigation, and real-world feedback from diverse populations.

Keywords: Artificial Intelligence, Health Equity, Medical Imaging, Healthcare Disparities, Digital Divide

ISSN 3069-8200

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