A new study suggests that an artificial intelligence system may be able to perform tasks as accurately as a highly trained radiologist. The paper published in the Journal of the National Cancer Institute outlines how an AI system can accurately detect evaluate digital mammography in breast cancer screenings. Breast cancer screenings are an important tool in the early detection of breast cancer and the reduction of breast cancer-related mortality. Screenings currently are very labor intensive due to the high volume of women needing scans. In some parts of the world, including the U.S. there is a scarcity in the number of highly trained breast screening radiologists which has led to the development of AI systems that can do some of the tasks related to evaluating mammograms.
One in three women over 50 has delayed or not attended their cervical screening test, which should take place every five years, according to a survey from a cervical cancer charity. The average delay was more than two years, but one in 10 put off the test for more than five years. Jo's Cervical Cancer Trust surveyed 1,000 women over 50. It said not attending cervical screening was the biggest risk factor to developing cervical cancer. The survey found a lack of understanding of cervical cancer and cancer screening among women in that age group.
Artificial intelligence is better than specialist doctors at diagnosing lung cancer, a US study suggests. The researchers at Northwestern University in Illinois and Google hope the technology could boost the effectiveness of cancer screening. Finding tumours at an earlier stage should make them easier to treat. The team said AI would have a "huge" role in the future of medicine, but the current software is not yet ready for clinical use. The study focused on lung cancer, which kills more people - 1.8 million a year - than any other type of cancer.
Google AI researchers working with Northwestern Medicine created an AI model capable of detecting lung cancer from screening tests better than human radiologists with an average of eight years experience. When analyzing a single CT scan, the model detected cancer 5% more often on average than a group of six human experts and was 11% more likely to reduce false positives. Humans and AI achieved similar results when radiologists were able to view prior CT scans. When it came to predicting the risk of cancer two years after a screening, the model was able to find cancer 9.5% more often compared to estimated radiologist performance laid out in the National Lung Screening Test (NLST) study. Detailed in research published today in Nature Medicine, the end-to-end deep learning model was used to predict whether a patient has lung cancer, generating a patient lung cancer malignancy risk score and identifying the location of the malignant tissue in the lungs.
These are perhaps the most powerful and important four words a woman can hear after a breast-screening visit. X-ray based mammography is an effective screening tool for detecting cancer, but what many women may not know is that breast screening programs produce a high level of false positive results, particularly after multiple years of screening. In other words, women are informed they may have cancer when in fact they don't. This is particularly true in the US, where each study is generally read by a single, expert radiologist. In Europe, two independent radiologists read each study.