Scientists from Weill Cornell Medicine and the Dana-Farber Cancer Institute in Boston have developed and tested new artificial intelligence (AI) tools tailored to digital pathology - a rapidly growing field that uses high-resolution digital images created from tissue samples to help diagnose disease and guide treatment.

Proteins have evolved to excel at everything from contracting muscles to digesting food to recognizing viruses. To engineer better proteins, including antibodies, scientists often iteratively mutate the amino acids - the units that are arranged in a sequence to make up proteins - in different positions until the resulting protein has an improved function, like eliciting a stronger immune response or capturing carbon dioxide from the atmosphere more efficiently.

If there is one medical exam that everyone in the world has taken, it's a chest x-ray. Clinicians can use radiographs to tell if someone has tuberculosis, lung cancer, or other diseases, but they can't use them to tell if the lungs are functioning well.

Until now, that is.

A study comparing jokes by people versus those told by ChatGPT shows that humans need to work on their material.

The research team behind the study published on Wednesday, July 3, in the journal PLOS ONE, took on the serious task of comparing participants’ reactions to jokes written by ChatGPT 3.5 and others written by people.

A new way to map the spread and evolution of pathogens, and their responses to vaccines and antibiotics, will provide key insights to help predict and prevent future outbreaks. The approach combines a pathogen's genomic data with human travel patterns, taken from anonymised mobile phone data.

Researchers from the Wellcome Sanger Institute, University of the Witwatersrand and National Institute for Communicable Diseases in South Africa, the University of Cambridge, and partners across the Global Pneumococcal Sequencing project, integrated genomic data from nearly 7,000 Streptococcus pneumoniae (pneumococcus) samples collected in South Africa with detailed human mobility data.

A new artificial intelligence (AI) tool that can help to select the most suitable treatment for cancer patients has been developed by researchers at The Australian National University (ANU).

DeepPT, developed in collaboration with scientists at the National Cancer Institute in America and pharmaceutical company Pangea Biomed, works by predicting a patient's messenger RNA (mRNA) profile.

Osteoporosis is so difficult to detect in early stage it’s called the "silent disease." What if artificial intelligence could help predict a patient’s chances of having the bone-loss disease before ever stepping into a doctor's office?

Tulane University researchers made progress toward that vision by developing a new deep learning algorithm that outperformed existing computer-based osteoporosis risk prediction methods, potentially leading to earlier diagnoses and better outcomes for patients with osteoporosis risk.