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Editor’s note: This piece is the fourth in a series of articles about AI in the laboratory. Read parts one, two, and three.  

PathAI has been working on AI algorithms to help with the interpretation of liver biopsies, for example. Currently, to diagnose severe nonalcoholic steatohepatitis (NASH), pathologists determine a score based on factors such as the amount of inflammation in the liver, the number of ballooning hepatocytes (diseased liver cells), and the degree of fibrosis in the liver, Dr. Beck explains. They then assess these criteria to deliver a score from zero to four. This is a difficult manual task without the assistance of an AI tool helping the pathologist to count each of these things, Dr. Beck says. 

“Instead of a pathologist acting alone and looking at it under the microscope without any assistance, the next step after staining is to put these slides into a whole slide imager to create large image files, and then essentially process those slides with an AI system whose job it is to do very similar to what you might ask a resident or trainee to do in theory.” However, the AI will be “far more exhaustive and accurate, and exact, than what any trainee could do,” Dr. Beck says. 

Predictive AI algorithms are also in development in a wide variety of use cases in pathology, according to Dr. Naveed Rabbani, MD, Fellow Physician in Clinical Informatics at Lucile Packard Children’s Hospital, Stanford Medicine. 

A pediatrician who works with pathologists, Dr. Rabbani is part of a research team at Stanford developing AI tools for “laboratory stewardship,” namely ordering the right test at the right time, and not over-ordering tests.  

They are developing machine learning algorithms that can predict the results of a blood test that hasn’t been done yet based on information in a patient’s medical record to identify if a test is worth doing or not.  
 
“This will help cut down on unnecessary blood tests, which is good for the health system, because it means less waste, and good for the patients who don’t have to be awoken in the morning for a blood test that might not be necessary,” Dr. Rabbani says. 

Given that many tests can be expensive, and that supplies to collect blood are in shortage, an algorithm like this can offer big savings and save patients from tests they don’t need. 

In similar research, Dr. Rabbani mentioned that algorithms are in the works that will one day offer guidance to physicians to suggest blood tests, as well, referred to as recommender algorithms. “Consider the scenario where a physician has a patient with a suspected diagnosis for which they have referred to a specialist. A recommender algorithm can help the physician order the necessary blood tests ahead of time, so that patient can have the tests done before they see the specialist and take full advantage of the visit the first time around. No time wasted.”

3. AI for tumor classification  

Even more exciting, AI may one day be used to create an interactive pathology report, Dr. Beck says. “So, imagine if you could ask questions of your pathology report, and get true answers back that are actually helpful in real time. You wouldn’t have to focus on sending an email to your doctor but have a lot of that interaction be done directly with the AI system that is super knowledgeable about your health history as well as what's going on in your particular case.” 

An explosion of growth 

In short, AI technology is experiencing “astronomical growth,” according to Joseph Rudolf, MD. While that does threaten some laboratory professionals, he suggests that this kind of growth is in sync with changes in the laboratories themselves. Dr. Rudolf is Assistant Professor of Clinical Pathology at the University of Utah School of Medicine, Medical Director of the Automated Core Laboratory, and Medical Director of Clinical Informatics at ARUP Laboratories.