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AI has entered the breast mammography field to improve cancer detection and reduce false positives. Despite its potential, AI screenings show significant disparities in effectiveness, particularly for Black women. The FDA's lack of diversity in training data contributes to these inequities, leading to poorer outcomes for underrepresented groups.
Experts emphasize the need for diverse datasets to enhance AI's accuracy in breast cancer detection. Initiatives like SAMBAI aim to create comprehensive databases to address these gaps. Additionally, new tools like MIRAI are being developed to better predict risks for high-risk demographics, ensuring that AI can serve all women effectively.
• AI screenings show bias against Black women in breast cancer detection.
• Diverse datasets are crucial for improving AI accuracy in healthcare.
AI refers to computer systems designed to perform tasks that typically require human intelligence, such as interpreting medical images.
An algorithm is a set of rules or calculations used by AI to analyze data and make predictions.
Machine learning is a subset of AI that enables systems to learn from data and improve over time without explicit programming.
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