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  • 2019-08-14

How to Lie with Statistics: Things To Keep in Mind While Evaluating a Deep Learning Claim


  • In today's age of deep learning and artificial intelligence, a radiologist must know what to watch out for while evaluating a deep learning algorithm's claim
  • What is ground truth?
  • Specific points to keep in mind while evaluating:
    • What is the medium of communication? Is it a video, a pre-print or a reputed peer-reviewed journal article?
    • What is the performance metric? Accuracy is a bad metric to use.
    • What data was the algorithm developed on? Generally, algorithms developed on poor ground truth have poor performance
    • What data was the algorithm validated on? Generally, algorithms validated on data from the same institution from where training data was obtained tend to falsely perform better
    • How much data was it tested on? Test data should not only be independent, but also adequate, both in number and disease heterogeneity.
    • What are the implications of the algorithm failing - what if a chest X-Ray algorithm misses a critical finding?
  • Try to get access to the actual algorithm and run it in your department


  • Why should a radiologist know how to evaluate a deep learning algorithm?
  • Performance metrics for evaluating algorithms
  • Data - training and testing
  • When an algorithm fails - implications
  • Run AI in your department!

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