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In the third blog in our series on artificial intelligence (AI) and machine learning (ML)-driven predictive models (data analytics tool or software) in health care, we discussed some potential risks (sometimes referred to as model harms) related to these emerging technologies and how these risks could lead to adverse impacts or negative outcomes. Given these potential risks, some have questioned whether they can trust the use of these technologies in health care.
We are encouraged to see that some stakeholders are demonstrating that a predictive model is fair, appropriate, valid, effective, and safe (FAVES), rather than amplifying biases or harms….