<ul data-eligibleForWebStory="true"><li>AI has the potential to revolutionize radiology, but ensuring accuracy and trustworthiness is crucial.</li><li>Algorithmic bias in AI-driven software can lead to clinical errors and disparities in performance.</li><li>A research team highlights pitfalls and solutions for addressing bias in AI radiology models.</li><li>Challenges include inadequate demographic data in medical image datasets and defining demographics like race and gender.</li><li>Generating synthetic imaging datasets using generative AI is proposed to improve bias measurement.</li><li>Consensus on defining bias and fairness metrics in radiology is critical for accurate evaluation.</li><li>Recommendations include improving demographic reporting, developing standardized analysis frameworks, and enhancing collaboration.</li><li>Efforts to mitigate bias require multidisciplinary communication and collective action from various stakeholders.</li><li>Collaboration is crucial to develop frameworks prioritizing patient safety and equitable outcomes in healthcare.</li></ul>

AI algorithms in radiology: how to identify and prevent inadvertent bias

Discover more