Radiologists and other clinicians should not yet depend on machine learning for diagnostic support because robust evidence is still lacking, experts wrote Thursday in JAMA Network Open.
Advances in mathematical modeling and computing power have led to an explosion in published artificial intelligence algorithms, with some claiming they can outshine human radiologists. But a group of U.K. researchers urges caution, with few randomized trials or prospective studies to support these assertions.
“This systematic review found no robust evidence that the use of [machine learning]-based algorithms was associated with better clinician diagnostic performance,” study co-author Stephan Ursprung, with the Department of Radiology at the University of Cambridge, and colleagues wrote March 11.
To reach their conclusions, Ursprung et al. queried medical literature databases for research logged between 2010 and 2019. They sought peer-reviewed studies, comparing clinician performance with and without the use of machine learning-based diagnostic clinical decision support systems. Out of 8,112 studies initially retrieved, researchers screened 5,154 abstracts before landing on 37 that met the inclusion criteria, with most pertaining to lung pathology or the diagnosis of cancer.
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