An inconsistent data set introduces biases and errors. This can have profound consequences for an AI model trained on such raw data. Biased and inaccurate data can perpetuate healthcare disparities and affect patient outcomes. What solutions have been proffered far in its resolution in order for AI to be effectively deployed in medicine and the medical sciences as well as the general healthcare landscape globally.
Hello, correct one of the biggest challenges for AI is to be able to obtain consistent data, but this depends on many factors such as: perception errors, sampling errors, errors in the registry, and that depend on the risk of the person. In the same sense, developing systems that are tolerant of this type of error is an alternative to this challenge.
Medical data in most cases requires confidentiality. Considering this factor, it is very difficult to get accurate data to ride on. Without an accurate data the inference or output that will be generated by the AI will be bias. Also, the variation in the ailments peculiarity across the continent is another major factor to get all encompassing AI that will address all it requires medically.
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