Ellison Institute uses AI to accelerate cancer diagnosis and treatment
Associated with
Margaret Lindquist Margaret Lindquist
Ellison Institute uses AI to accelerate cancer diagnosis and treatment
Read now

Nearly 150 years after the introduction of modern tissue staining to detect cancer, doctors are still diagnosing the disease much the way they did then. They still look at each biopsy sample under a microscope and hone in on suspicious areas, although new technologies are now allowing them to identify the molecular markers, like DNA mutations, that indicate whether a patient would respond best to one therapy or another.

But that diagnostic odyssey is beginning to change, thanks in part to groundbreaking research conducted at the Lawrence J. Ellison Institute for Transformative Medicine of USC. There, the introduction of artificial intelligence into cancer research and treatment has the potential to revolutionize both oncology fields, just as AI is doing in transportation (self-driving cars and self-flying planes), education (chatbot advisers), finance (predictive investment models), and a range of other sectors.

"I asked a pathologist, 'If there's one thing a computer could do for you, what would it be?' He said, 'I want it to tell me where to look,'" says Dr. Dan Ruderman, director of analytics and machine learning at the institute and assistant professor of research medicine at the Keck School of Medicine at the University of Southern California. "Digital pathology is how the medical field is going to catch up with Silicon Valley. Computers are showing us that they can see things we can't, so we're taking these techniques that have been perfected for other domains and transferring all that knowledge over to pathology."

The institute is scanning slides that show slices of biopsies and analyzing that visual data with AI algorithms trained to recognize areas of concern. After enough training, the algorithm will be able to not only recognize cancer, but even recommend a course of treatment. The use of deep learning and neural networks will make sophisticated diagnoses available even in developing countries, where there may be only one doctor in a region who has experience with diagnosing cancer.

"There's a basic diagnostic slide that is taken for every patient-but not every place has someone who can read the slide and determine the subtype of cancer and be able to recommend a specific course of drugs," Dr. Ruderman says. "If the AI can handle that, it will not only save these hospitals money, but it should also result in better patient care."