What is artificial intelligence?
Artificial intelligence refers to computer programs, or algorithms, that use data to make decisions or predictions. To build an algorithm, scientists might create a set of rules, or instructions, for the computer to follow so it can analyze data and make a decision.
For example, Dr. Turkbey and his colleagues used existing rules about how prostate cancer appears on an MRI scan. They then trained their algorithm using thousands of MRI studies—some from people known to have prostate cancer, and some from people who did not.
With other artificial intelligence approaches, like machine learning, the algorithm teaches itself how to analyze and interpret data. As such, machine learning algorithms may pick up on patterns that are not readily discernable to the human eye or brain. And as these algorithms are exposed to more new data, their ability to learn and interpret the data improves.
Researchers have also used deep learning, a type of machine learning, in cancer imaging applications. Deep learning refers to algorithms that classify information in ways much like the human brain does. Deep learning tools use “artificial neural networks” that mimic how our brain cells take in, process, and react to signals from the rest of our body.
Research on AI for cancer imaging
Doctors use cancer imaging tests to answer a range of questions, like: Is it cancer or a harmless lump? If it is cancer, how fast is it growing? How far has it spread? Is it growing back after treatment? Studies suggest that AI has the potential to improve the speed, accuracy, and reliability with which doctors answer those questions.
Approach diagnoses cancer in under 3 minutes during surgery.
“AI can automate assessments and tasks that humans currently can do but take a lot of time,” said Hugo Aerts, Ph.D., of Harvard Medical School. After the AI gives a result, “a radiologist simply needs to review what the AI has done—did it make the correct assessment?” Dr. Aerts continued. That automation is expected to save time and costs, but that still needs to be proven, he added.
In addition, AI could make image interpretation—a highly subjective task—more straightforward and reliable, Dr. Aerts noted.
Complex tasks that rely on “a human making an interpretation of an image—say, a radiologist, a dermatologist, a pathologist —that’s where we see enormous breakthroughs being made with deep learning,” he said.
But what scientists are most excited about is the potential for AI to go beyond what humans can currently do themselves. AI can “see” things that we humans can’t, and can find complex patterns and relationships between very different kinds of data.
“AI is great at doing this—at going beyond human performance for a lot of tasks,” Dr. Aerts said. But, in this case, it is often unclear how the AI reaches its conclusion, so it’s difficult for doctors and researchers to check if the tool is performing correctly.
Tests like mammograms and Pap tests are used to regularly check people for signs of cancer or precancerous cells that can turn into cancer. The goal is to catch and treat cancer early, before it spreads or even before it forms at all.
Scientists have developed AI tools to aid screening tests for several kinds of cancer, including breast cancer. AI-based computer programs have been used to help doctors interpret mammograms for more than 20 years, but research in this area is quickly evolving.
One group created an AI algorithm that can help determine how often someone should get screened for breast cancer. The model uses a person’s mammogram images to predict their risk of developing breast cancer in the next 5 years. In various tests, the model was more accurate than the current tools used to predict breast cancer risk.
NCI researchers have built and tested a deep learning algorithm that can identify cervical precancers that should be removed or treated. In some low-resource settings, health workers screen for cervical precancer by inspecting the cervix with a small camera. Although this method is simple and sustainable, it is not very reliable or accurate.
Mark Schiffman, M.D., M.P.H., of NCI’s Division of Cancer Epidemiology and Genetics, and his colleagues designed an algorithm to improve the ability to find cervical precancers with the visual inspection method. In a 2019 study, the algorithm performed better than trained experts.
For colon cancer, several AI tools have been shown in clinical trials to improve the detection of precancerous growths called adenomas. However, because only a small percentage of adenomas turn into cancer, some experts are concerned that such AI tools could lead to unnecessary treatments and extra tests for many patients.
