Opinion: Four types of bias in medical AI are running under the FDA’s radar

Although artificial intelligence is entering health care with great promise, clinical AI tools are prone to bias and real-world underperformance from inception to deployment, including the stages of dataset acquisition, labeling or annotating, algorithm training, and validation. These biases can reinforce existing disparities in diagnosis and treatment.

To explore how well bias is being identified in the FDA review process, we looked at virtually every health care AI product approved between 1997 and October 2022. Our audit of data submitted to the FDA to clear clinical AI products for the market reveals major flaws in how this technology is being regulated.

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