Ever wonder if the latest and greatest artificial intelligence (AI) tool you read about in the morning paper is going to save your life? A new study published in JAMA led by John W. Ayers, Ph.D., of the Qualcomm Institute within the University of California San Diego, finds that question can be difficult to answer since AI products in healthcare do not universally undergo any externally evaluated approval process assessing how it might benefit patient outcomes before coming to market.

The application of AI in precision oncology has so far been largely confined to the development of new drugs and had only limited impact on the personalisation of therapies. New AI-based approaches are increasingly being applied to the planning and implementation of personalised drug and cell therapies.

The American College of Radiology® (ACR®), working in close collaboration with four other radiology societies from around the world, have issued a joint statement on the development and use of artificial intelligence (AI) tools in radiology. This groundbreaking joint statement is openly available in ACR's Journal of the American College of Radiology, published by Elsevier.

Chemists of the University of Amsterdam (UvA) have developed an autonomous chemical synthesis robot with an integrated AI-driven machine learning unit. Dubbed 'RoboChem', the benchtop device can outperform a human chemist in terms of speed and accuracy while also displaying a high level of ingenuity.

In a groundbreaking study published on January 18, 2024, in Cancer Discovery, scientists at University of California San Diego School of Medicine leveraged a machine learning algorithm to tackle one of the biggest challenges facing cancer researchers: predicting when cancer will resist chemotherapy.

All cells, including cancer cells, rely on complex molecular machinery to replicate DNA as part of normal cell division.

Artificial intelligence (AI) has the potential to detect rheumatic heart disease (RHD) with the same accuracy as a cardiologist, according to new research demonstrating how sophisticated deep learning technology can be applied to this disease of inequity. The work could prevent hundreds of thousands of unnecessary deaths around the world annually.

This review was jointly published by Prof. Long-Jiang Zhang (Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University) and Prof. Christian Tesche (Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina and Department of Cardiology, Munich University Clinic, Ludwig-Maximilian-University).