Share
))
HBO and The Ringer's Bill Simmons hosts the most downloaded sports podcast of all time, with a rotating crew of celebrities, athletes, and media staples, as well as mainstays like Cousin Sal, Joe House, and a slew of other friends and family members who always happen to be suspiciously available.
…
continue reading
Manage episode 505730947 series 3678442
Content provided by Stephen Auger. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Stephen Auger or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
How do we know if a medical AI has truly learned to spot disease, or just memorised the answers to its practice questions? The same way we evaluate a trainee: with a final, unseen exam.
This crucial process involves splitting data into three sets: training data (the textbook), validation data (the mock exam), and test data (the final exam). In this episode of The Health AI Brief, we explain why this split is our best defence against overconfident AI, what 'overfitting' means for clinical practice, and why the 'test set' result is the only number you should trust when appraising a new AI study.
#TrainingData #ValidationData #TestData #Overfitting #ModelValidation #ArtificialIntelligence #MachineLearning #HealthcareAI #MedicalAI #ClinicalAI #CriticalAppraisal #EvidenceBasedMedicine #DigitalHealth #ai in medicine Music generated by Mubert https://mubert.com/render
51 episodes
