JAMA Medical News
AI and Clinical Practice—AI Monitoring to Reduce Data-Based Disparities
Jan 3, 2024
Arvind Narayanan, professor of computer science at Princeton, joins JAMA's Editor in Chief Kirsten Bibbins-Domingo to explore AI fairness, transparency, and accountability in healthcare. They discuss the racial bias in hospital algorithms, limitations of AI technologies, and the need for constant monitoring. The podcast also highlights the challenges of data availability, privacy concerns, and the potential of generative AI for self-diagnosis.
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Quick takeaways
- Automated AI systems in patient care can perpetuate biases, prioritizing certain racial groups over others based on historical disparities.
- Data availability disparities can lead to inaccurate AI models for minority populations, but oversampling and using synthetic data can help improve performance.
Deep dives
Bias in AI-driven patient care
AI-driven automation in patient care can lead to inherent biases, perpetuating structural inequalities. For example, a study revealed that an algorithm used by hospitals to target interventions was more likely to prioritize white patients over black patients, as it was trained on past data reflecting the historical spending disparities between the two groups. The danger lies in magnifying these biases and the difficulties in changing them once automated.
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