Much is made about AI’s potential to improve radiologists’ efficiency and boost their detection capabilities, but when it comes to cancer care, a few experts believe the coming tech revolution may face some problems.
That was the sentiment expressed by pathologists from the University of Texas in Austin and Brigham and Women’s Hospital in a Dec. 12 New England Journal of Medicine perspective. Machine learning, they wrote, will certainly help clinicians interpret more images with better accuracy, but it can’t solve the problem at the heart of diagnosing cancer: the lack of a histopathological “gold standard.”
“Diagnoses of early-stage cancer made using machine learning algorithms will undoubtedly be more consistent and more replicable than those based on human interpretation,” Adewole S. Adamson, MD, with the Texas institution’s division of dermatology, and colleagues wrote. “But they won’t necessarily be closer to the truth—that is, algorithms may not be any better than humans at determining which tumors are destined to cause symptoms or death.”
This is because there is no single answer to the question, they argued. Clinically, physicians are interested in cancer as a dynamic process, which begins as a tumor that may spread and cause symptoms if left untreated. Pathologically, however, identifying cancer requires static observation, achieved by examining individual cells, surrounding tissue, biomarkers and the relationship between the three.
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