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Artificial Intelligence models to analyse cancer images take shortcuts that introduce bias


Artificial intelligence (AI) tools and deep learning models are a powerful tool in cancer treatment. They can be used to analyse digital images of tumour biopsy samples, helping doctors quickly classify the type of cancer, predict prognosis and guide a course of treatment for the patient. However, unless these algorithms are properly calibrated, they can sometimes make inaccurate or biased predictions.

A new study led by researchers from the University of Chicago shows that deep learning models trained on large sets of cancer genetic and tissue histology data can easily identify the institution that submitted the images. The models, which use machine learning methods to "teach" themselves how to recognise certain cancer signatures, end up using the submitting site as a shortcut to predicting outcomes for the patient, lumping them together with other patients from the same location instead of relying on the biology of individual patients. This in turn may lead to bias and missed opportunities for treatment in patients from racial or ethnic minority groups who may be more likely to be represented in certain medical centres and already struggle with access to care.

"We identified a glaring hole in the in the current methodology for deep learning model development which makes certain regions and patient populations more susceptible to be included in inaccurate algorithmic predictions," said Alexander Pearson, one of the authors of the study.

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Source: Digital Health News, 22 July 2021

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