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Bias Recognition and Mitigation Strategies in Artificial Intelligence Healthcare Applications
Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. They highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance.As of May 13, 2024, the Food and Drug Administration (FDA) update indicated an unprecedented surge in the approval of AI-enabled Medical Devices, listing 191 new entries while reaching a total of 882, predominantly in the field of radiology (76%), followed by cardiology (10%) and neurology (4%).
Medigy Insights
The dominant origin of biases observed in healthcare AI are human. While rarely introduced deliberately, these reflect historic or prevalent human perceptions, assumptions, or preferences that can manifest across various future stages of AI model development, potentially with profound impact. For example, data collection activities influenced by human bias can lead to the training of algorithms that replicate historical healthcare inequalities, leading to cycled reinforcement where past injustices are perpetuated into future practice.
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