The new findings are published in The Lancet Digital Health. The initial results of the Mammography Screening with Artificial Intelligence (MASAI) study* - a randomised trial to evaluate whether AI can improve mammography screening - were published in August 2023. The study started in spring 2021, and the final report will be written next year. A second report has now been published, and Kristina Lång, who is responsible for the study, is pleased to be able to show strong figures.

Personalized medicine aims to tailor treatments to individual patients. Until now, this has been done using a small number of parameters to predict the course of a disease. However, these few parameters are often not enough to understand the complexity of diseases such as cancer. A team of researchers from the Faculty of Medicine at the University of Duisburg-Essen (UDE), LMU Munich, and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin has developed a new approach to this problem using artificial intelligence (AI).

Researchers from the Cleveland Clinic Genome Center have successfully applied advanced artificial intelligence (AI) genetics models to Parkinson's disease. Researchers identified genetic factors in progression and FDA-approved drugs that can potentially be repurposed for PD treatment.

The npj Parkinson's Disease report uses an approach called “systems biology,” which uses AI to integrate and analyze multiple different forms of information from genetic, proteomic, pharmaceutical and patient datasets to identify patterns that may not be obvious from analyzing one form of data on its own.

As the fields of healthcare and technology increasingly evolve and intersect, researchers are collaborating on the best ways to use emerging technologies such as artificial intelligence (AI) to care for patients.

This includes using AI to assist in collecting and deciphering diagnostic data among medical professionals, particularly in underserved communities.

A new deep learning model shows promise in detecting and segmenting lung tumors, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA). The findings of the study could have important implications for lung cancer treatment.

According to the American Cancer Society, lung cancer is the second most common cancer among men and women in the U.S. and the leading cause of cancer death.

Machine learning and artificial intelligence (AI) techniques and analysis of large datasets have helped University of Birmingham researchers to discover proteins that have strong predictive potential for colorectal cancer.

In a paper published in Frontiers in Oncology, researchers analysed one of the largest UK Biobank dataset of protein profiles from healthy individuals and colorectal cancer patients and highlighted three proteins - TFF3, LCN2, and CEACAM5 - as important markers linked to cell adhesion and inflammation, processes closely associated with cancer development.

Intensive care units (ICUs) face mounting pressure to effectively manage resources while delivering optimal patient care. Groundbreaking research published in the INFORMS journal Information Systems Research highlights how a novel artificial intelligence (AI) model is revolutionizing ICU care by not only improving predictions of patient length of stay, but also equipping clinicians with clear, evidence-based insights to guide critical decisions.