Clinical utility, not ‘prettiness’

Engineers determine best metrics for evaluating AI improvements to medical imaging

Recent advances in artificial intelligence (AI) have opened the door to using AI-based methods for denoising, or cleaning up, medical images.

However, before these tools can be used in clinical settings for real patient care, they need to be rigorously evaluated, said Abhinav Jha, an assistant professor of biomedical engineering at the McKelvey School of Engineering and of radiology at the Mallinckrodt Institute of Radiology (MIR) in the School of Medicine, both at Washington University in St. Louis.

In a study published in Medical Physics, Jha and collaborators at MIR evaluated a commonly used AI-based approach to denoise cardiac SPECT images. Their findings demonstrate the need for evaluating AI algorithms on clinical tasks, and not just relying on visual similarity as a measure of performance.

Read more on the McKelvey School of Engineering website.

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