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Lots of Hospitals Are Using AI. Few Are Testing For Bias
Feb 27, 2025
In this discussion, Paige Nong, an Assistant Professor at the University of Minnesota specializing in AI's influence on healthcare, reveals the current landscape of AI use in hospitals. She highlights the concerning lack of bias testing in predictive algorithms, particularly those affecting marginalized patients. The conversation emphasizes the urgent need for consistent governance to ensure equitable treatment. Nong also addresses challenges faced by safety net hospitals and calls for robust evaluations of AI tools to enhance patient experiences and support health equity.
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Quick takeaways
- While many hospitals are adopting AI technologies to enhance patient care, the lack of monitoring for bias significantly jeopardizes healthcare equity.
- Research reveals alarming bias in predictive algorithms used in healthcare, favoring certain demographics and raising urgent questions about patient welfare.
Deep dives
The Rapid Rise of AI in Healthcare
Artificial intelligence (AI) is increasingly integrated into healthcare, enabling programs to assist in answering patient queries and diagnosing illnesses. Despite the hype surrounding AI's capabilities, many hospitals have just begun to adopt and evaluate its implications deeply. Current data on AI usage indicates that while a significant portion of hospitals employs these technologies, the monitoring for bias and effectiveness remains inadequate. With over 350 gigabytes of data per patient being processed, there is rising concern about the potential risks to patient care and the reliability of these AI tools.
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