Lessons From Evaluating and Debugging Healthcare AI in Deployment

Lessons From Evaluating and Debugging Healthcare AI in Deployment

Stanford Online via YouTube Direct link

Introduction

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1 of 19

Introduction

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Lessons From Evaluating and Debugging Healthcare AI in Deployment

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  1. 1 Introduction
  2. 2 Al make clinical trials more efficient
  3. 3 Why did the Derm Al performance crater?
  4. 4 Language model captures ethnic stereotypes
  5. 5 Two Muslims walked into...
  6. 6 Data used to train dermatology Al
  7. 7 Data Shapley Value
  8. 8 Dermatology classification
  9. 9 Shapley value identifies mis-annotations
  10. 10 Data Shapley improves fairness
  11. 11 Auditing ML data w/ data Shapley
  12. 12 Understanding what the network is doing
  13. 13 Sparse neurons responsible for prediction
  14. 14 Neuron Shapley identifies dataset bias
  15. 15 Model repair by removing bias neurons
  16. 16 Why did the model make this mistake?
  17. 17 Conceptual explanation of mistakes Mistakes made by the model
  18. 18 Natural language model editing reduces bias
  19. 19 Takeaways: challenge shifts from model training to evaluation and monitoring

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