Pathology News Roundup: March 9, 2021

A team of doctors led by UVA Health’s James H. Harrison Jr., MD, PhD, published an article in the Archives of Pathology & Laboratory Medicine, the official journal of the College of American Pathologists (CAP). The article outlines the work of Dr. Harrison and other members of the CAP’s Machine Learning Workgroup, who have spent the last two years evaluating the potential of AI.

 

In their article, the authors describe some amazing possibilities – from an “augmented reality” microscope that automatically identifies and labels important aspects in the field of view in real time to complete diagnostic image classification systems. 

 

In addition to predicting what the future may hold, the authors describe potential obstacles and make important recommendations for how the healthcare field can best capitalize on the technology’s awesome potential.

“AI and especially machine-learning algorithms introduce a fundamentally new kind of data analysis into the health-care workflow,” the authors write. “By virtue of their influence on pathologists and other physicians in selection of diagnoses and treatments, the outputs of these algorithms will critically impact patient care.”

 

Right now, pathology and other health-care applications of artificial intelligence are in their infancy. The federal Food and Drug Administration has approved only a few AI devices for pathology use, mostly for classifying cells in blood and body fluids and for screening cervical tissue, the authors report. But in research labs, scientists are using machine learning to classify and grade lung and prostate cancer, predict outcomes in lung and brain cancers, measure breast cancer proliferation, predict bladder cancer reoccurrence and much more. The authors describe what they’re seeing in research publications and early prototypes as “tantalizing.”

Dr. James Harrison. Image courtesy of UVA Health.

“Artificial intelligence systems, especially machine learning systems that perform complex image classification, are expected to have significant impact in two areas in which images particularly important, pathology and radiology,” said Dr. Harrison, director of Clinical Laboratory Informatics at UVA Health and a member of UVA’s Department of Pathology. “Pathologists will likely need to choose, verify, deploy, use and monitor AI systems in the future, and therefore they need to learn the strengths and weaknesses of these types of systems and techniques for their effective management.”

In addition to projections of future applications, Harrison and his colleagues provide an overview of existing AI algorithms and discuss the development and validation of systems that use AI. Their review also discusses potential concerns about clinical implementation of the technologies, emphasizing the importance of careful validation and performance monitoring to ensure AI is used safely and effectively. The article suggests potential regulations that may be needed along the way. “Creation of a regulatory framework with defined best practices for accomplishing these goals is a necessary step for successful dissemination of machine learning in pathology and medicine,” the authors write.