16.9 million people in the U.S. are cancer survivors living with, through, and beyond the diagnosis. Every survivor faces unique challenges and has different healthcare experiences. Early detection is the best way to minimize the costly course of cancer treatment and, most importantly, give individuals the best chance of survival. In this week’s Domain Knowledge, we look at how AI in oncology is making a positive impact on the patient’s cancer journey.

Cancer screenings involve combining medical imaging tests with the patient’s medical history to determine risk or make a diagnosis. Between the technical procedures and communication of the results to the appropriate medical professionals, there is substantial room for error. AI is a powerful tool that is capable of speeding up image classification and optimizing workflow to reduce errors of omission and reach a diagnosis sooner.

Some cancers, like colon cancer and melanoma, have a high 5-year survival rate when diagnosed early. However, these and other cancers can benefit from AI. Prostate cancer, for example, could see significant improvements in MRI screening efficiency by incorporating new AI technology. Patients would benefit from faster, more accurate diagnosis, and the radiologist workload would be lightened by not having to manually review each scan for prostate lesions.

Another recent study finds that AI is able to accurately predict whether thyroid nodules found on ultrasounds are cancerous. Thyroid cancer is highly treatable when detected early and with the use of this new technology, patients could see further improvement in survival and recovery rates.

The benefits of AI are even more apparent with lower survival rate types of cancer. AI capable of predicting the cancer risk of lung nodules from x-rays could make all the difference in diagnosing patients before cancer metastasizes to other parts of the body.

Colon cancer is another low survival rate form of cancer when detected at a later stage, but new AI-powered GI Genius devices supplement colonoscopies to increase the detection of precancerous polyps. These devices were even distributed to underserved areas as a priority to deliver clinical impact as soon as possible.

Pancreatic cancer is not typically detected until a later stage because there are few visible symptoms until the cancer has progressed. Takayuki Baba discusses his research on how AI is unlocking the possibility of early pancreatic cancer detection through CT scans. He believes AI is an indispensable element in solving the biggest problems in the fight against cancer.

That’s a wrap for this week’s review of news and happenings in the healthcare AI space. In closing, I’ll leave you with an invitation to learn more about the benefits of using an AI Hub to manage multiple applications across your clinical needs and offer you a personal demo of Ferrum’s platform and growing AI catalog.

If you have AI tips, suggestions, or resources you’d like to share leave us a note below, and please feel free to suggest topics you would like to see covered in future posts.