By implementing artificial intelligence into cancer care, machine learning tools can detect cancer, assist in decision-making, and recommend treatment approaches.




As artificial intelligence (AI) continues to grow in the healthcare field, researchers are findings new ways to utilize its capabilities. In chronic disease management and prevention, especially in cancer research, AI has been critical in the diagnosis, decision-making, and treatment process.


According to the National Cancer Institute, AI, machine learning, and deep learning can all be used to improve cancer care and patient outcomes.

“Integration of AI technology in cancer care could improve the accuracy and speed of diagnosis, aid clinical decision-making, and lead to better health outcomes. AI-guided clinical care has the potential to play an important role in reducing health disparities, particularly in low-resource settings,” NCI wrote on Cancer Detection & Diagnosis Research.

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With the use of AI, researchers can create the next stage of precision oncology.


Recently, medical professionals have expanded the use of AI capabilities in cancer detection. At Tulane University, researchers discovered that AI can accurately detect and diagnose colorectal cancer by analyzing tissue scans as well or better than pathologists.

The researchers gathered over 13,000 images of colorectal cancer from 8,803 subjects and 13 independent cancer centers in China, Germany, and the United States. Then, using images that technicians selected at random, the researchers built a machine learning program.

The program can recognize images of colorectal cancer, which according to researchers, is one of the most common causes of cancer-related deaths in Europe and the US.

After creating a performance measurement tool, the team of researchers compared the machine learning technique to the work done by the pathologists. The study indicated that the average pathologist scored around 0.969 for accuracy when identifying colorectal cancer, while the program scored 0.98, proving to be slightly more accurate than the manual data by pathologists.

According to researchers, there hope is that the study will encourage pathologists to use more prescreening technology to speed up diagnosis.

Not only can AI catch cancer earlier, but it can also improve detection accuracy. New York University researchers created an AI program trained to identify patterns among thousands of breast ultrasound images to aid physicians in diagnosing.

When tested on 44,755 completed ultrasound exams, the AI tool increased radiologists’ ability to accurately identify breast cancer by 37 percent. Additionally, the tool helped to reduce the number of tissue samples and biopsies necessary to confirm tumors by 27 percent.

“Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign,” study senior investigator Krzysztof Geras, PhD, said in a press release.

AI can also advance existing technology to improve patient outcomes. According to a recent study, medical professionals can use AI technology to quickly and accurately sort through breast MRIs in patients with dense breast tissue to eliminate those without cancer.

While mammography plays an important role in reducing breast cancer-related deaths, it is less sensitive in women with extremely dense breast tissue. Additionally, women with extremely dense breasts are three to six times more likely to develop breast cancer than women with almost entirely fatty breasts and two times more likely than the average woman.

According to researchers, by combining mammography capabilities and AI, the technology can significantly reduce the workload for radiologists, and improve patient outcomes.


Predictive models have become a critical element in cancer treatment. By identifying risk factors, predictive models can determine an individual’s likelihood of developing certain cancers. Medical professionals can then encourage patients to engage in preventive care strategies.

According to University of Hawaii researchers, deep learning can distinguish between the mammograms of women who will later develop breast cancer and those who will not.

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