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- W4293750892 abstract "Abstract This chapter investigates data collection and curation activities that precede the training of artificially intelligent systems. The chapter documents different approaches to annotating data to create what is called “ground truth” in machine learning. It begins with an exploration of different techniques for health artificial intelligence (AI) “groundtruthing.” This includes collection of data from electronic health records, the use of established diagnostic techniques, and custom annotations by healthcare professionals. The chapter then investigates different statistical techniques for validating AI performance and describes the results of an AI development simulation that models the search for ground truth and exposes serious limitations in the most common research designs." @default.
- W4293750892 created "2022-08-31" @default.
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- W4293750892 date "2022-10-02" @default.
- W4293750892 modified "2023-09-25" @default.
- W4293750892 title "The Search for Ground Truth" @default.
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- W4293750892 doi "https://doi.org/10.1093/oso/9780197644461.003.0005" @default.
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