Artificial intelligence that scans GP records to find hidden patterns has helped doctors detect significantly more cancer cases. The rate of cancer detection rose from 58.7% to 66.0% at GP practices using the "C the Signs" AI tool. This analyses a patient's medical record to pull together their past medical history, test results, prescriptions and treatments, as well as other personal characteristics that might indicate cancer risk, such as their postcode, age and family history. It also prompts GPs to ask patients about any new symptoms, and if the tool detects patterns in the data that indicate a higher risk of a particular type of cancer, then it recommends which tests or clinical pathway the patient should be referred to. C the Signs is used in about 1,400 practices in England – about 15% – and was tested in 35 practices in the east of England in May 2021, covering a population of 420,000 patients.

Breast cancer continues to be a significant cause of mortality among women globally. Timely identification and precise diagnosis of breast abnormalities are critical for enhancing patient prognosis. In this study, we focus on improving the early detection and accurate diagnosis of breast abnormalities, which is crucial for improving patient outcomes and reducing the mortality rate of breast cancer. To address the limitations of traditional screening methods, a novel unsupervised feature correlation network was developed to predict maps indicating breast abnormal variations using longitudinal 2D mammograms. The proposed model utilizes the reconstruction process of current year and prior year mammograms to extract tissue from different areas and analyze the differences between them to identify abnormal variations that may indicate the presence of cancer. The model is equipped with a feature correlation module, an attention suppression gate, and a breast abnormality detection module that work together to improve the accuracy of the prediction. The proposed model not only provides breast abnormal variation maps, but also distinguishes between normal and cancer mammograms, making it more advanced compared to the state-of the-art baseline models. The results of the study show that the proposed model outperforms the baseline models in terms of Accuracy, Sensitivity, Specificity, Dice score, and cancer detection rate.

The use of adjunctive artificial intelligence (AI) doubled the positive predictive value (PPV) of digital mammography (DM) exams overall and led to greater than 90 percent accuracy for DM and digital breast tomosynthesis (DBT) in detecting breast cancer in women with elevated risk, according to research findings presented recently at the European Congress of Radiology (ECR) conference in Vienna, Austria. For the study, researchers compared the use of adjunctive AI (Transpara version 1.7.0, ScreenPoint Medical) in 11,988 women (between the ages of 50 and 74) who had DM or DBT screening exams versus 16,555 women screened with DM or DBT the year before without AI support. In the AI group, 5,049 women had DM screening with the Hologic Selenia device and 6,949 women had DBT screening with the Hologic Selenia Dimensions device, according to the study. For the non-AI cohort, 7,229 women had DM screening and 9,326 women had DBT.