JAMA Medical News
AI and Clinical Practice—AI and the Ethics of Developing and Deploying Clinical AI Models
Feb 7, 2024
Marzyeh Ghassemi, MIT professor, discusses ethical AI in clinical practice. Topics include biases in AI, disparities in model performance, data quality issues, labeling data correctly, ethical responsibilities of developers, and the dangers of automation bias in healthcare technology.
24:18
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Quick takeaways
- Recognizing biases in human-driven data collection processes is crucial for developing fair and effective AI models.
- Ethical machine learning principles are essential for responsible deployment of AI in healthcare settings.
Deep dives
Understanding Biases in Human-Driven Data Collection
One key insight from the podcast is the recognition of biases perpetuated through human-driven data collection processes. The conversation highlights the need to examine how models developed in various fields, including healthcare, may fail to work well for different groups due to unequal practices and biases present in the data. It is crucial to ensure robustness in algorithmic models to ensure they perform well across different environments and diverse populations.
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