Data limitations, evaluation issues, and publishing incentives may be slowing the clinical progress of machine learning in medical imaging, a new study finds.
– Though research on machine learning use in medical imaging has grown significantly in recent years, improvements in the clinical use of such data remain limited, according to a study published in npj Digital Medicine.
Machine learning (ML) is a promising but controversial tool for healthcare providers. Studies suggest heightened enthusiasm around the potential application of ML in clinical settings, but they also note that appropriate regulations must be implemented to ensure that it is effectively implemented. Recent studies have shown that biases within artificial intelligence (AI) algorithms can create health disparities.
The current study’s authors found that at each step of the research process, potential challenges and biases can be introduced that limit the clinical use of ML in medical imaging. Issues can arise from the beginning, depending on how data for this research are collected, how datasets are created and distributed, and what biases may exist in the datasets themselves.