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Effect Size

Effect size is a population parameter. It describes the population. In a sample, the ideal one would be zero if there are no differences between the two groups. It would increase either in a positive or a negative direction as either p1 becomes much better than p2 in the positive direction, or p2 is much better than p1 in the negative direction.

If I randomly take a subject from the p1 population and I randomly take a subject from the p2 population, what’s the probability that the p1 subject is better than the p2 minus the probability that p2 is better than p1? If that number is 0, that difference between them, and that’s what this idea is, that number is 0, that’s going to be because there really isn’t any overall difference between the two populations. 

To learn more from Dr. Helena Kraemer listen to the podcast episode below.

Effect Size Part 1

Effect Size Part 2

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Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).