Creating artificial life is a recurring theme in both science and popular literature, where it conjures images of creeping slime creatures with malevolent intentions or super-cute designer pets. At the same time, the question arises: What role should artificial life play in our environment here on Earth, where all life forms are created by nature and have their own place and purpose?

A machine learning model equipped with only data on people's age, smoking duration and the number of cigarettes smoked per day can predict lung cancer risk and identify who needs lung cancer screening, according to a new study publishing October 3rd in the open access journal PLOS Medicine by Thomas Callender of University College London, UK, and colleagues.

To discover new treatments for genetic disorders, scientists need a thorough knowledge of prior literature to determine the best gene/protein targets and the most promising drugs to test. However, biomedical literature is growing at an explosive rate and often contains conflicting information, making it increasingly time-consuming for researchers to conduct a complete and thorough review.

A device has been created that could carry out Clinical Breast Examinations (CBE).

The manipulator, designed by a team at the University of Bristol and based at the Bristol Robotics Laboratory, is able to apply very specific forces over a range similar to forces used by human examiners and can detect lumps using sensor technology at larger depths than before.

The Premier Digital Health Event of 2023 is about to unfold. Mark your calendars for November 9th and 10th as the fourth edition of the DigiHealthDay unveils. This prominent event is hosted by the Deggendorf Institute of Technology - European Campus Rottal-Inn (DIT-ECRI) in collaboration with their dedicated PARTNERS and SPONSORS.

In a study of more than 2,000 chest X-rays, radiologists outperformed AI in accurately identifying the presence and absence of three common lung diseases, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA).

Artificial intelligence (AI) and machine learning (ML) can effectively detect and diagnose Polycystic Ovary Syndrome (PCOS), which is the most common hormone disorder among women, typically between ages 15 and 45, according to a new study by the National Institutes of Health. Researchers systematically reviewed published scientific studies that used AI/ML to analyze data to diagnose and classify PCOS and found that AI/ML based programs were able to successfully detect PCOS.