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"Leveraging Data Analytics to Foster Equity in Healthcare"

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Artificial Intelligence (AI) holds great promise in making healthcare more affordable, effective and accessible for all patients. However, as the development and deployment of AI solutions in healthcare data have grown, so have the number of concerns related to their performance, reliability and ethical implications. These inherent biases can manifest in various ways, such as disparities in diagnosis, treatment recommendations or access to healthcare resources, particularly for historically marginalized or underrepresented populations.

By ensuring diverse representation in your AI development process and continued monitoring, healthcare providers can reduce predictive model bias, generate cost savings, and improve patient outcomes.

In this white paper you’ll learn about:

• How to recognize and account for bias in healthcare AI
• What development lifecycle practices to implement to remediate bias such as cross-functional teams and solution monitoring
• The benefits of working with healthcare tech partners like Diagnostic Robotics to build safeguards in AI algorithm models.

Offered Free by: Diagnostic Robotics
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