Collecting images of suspicious-looking skin growths and sending them off-site for specialists to analyze is as accurate in identifying skin cancers as having a dermatologist examine them in person, a new study shows.

According to the study authors, the findings add to evidence that such technology could help to reliably address diagnostic and treatment disparities for lower-income populations with limited access to dermatologists.

As artificial intelligence (AI) becomes more prevalent in health care, organizations and clinicians must take steps to ensure its safe implementation and use in real-world clinical settings, according to an article co-written by Dean Sittig, PhD, professor with McWilliams School of Biomedical Informatics at UTHealth Houston and Hardeep Singh, MD, MPH, professor at Baylor College of Medicine.

A new paper in Biology Methods and Protocols, published by Oxford University Press, shows that scientists can train artificial intelligence (AI) models to distinguish brain tumors from healthy tissue. AI models can already find brain tumors in MRI images almost as well as a human radiologist.

Researchers have made sustained progress in artificial intelligence (AI) for use in medicine. AI is particularly promising in radiology, where waiting for technicians to process medical images can delay patient treatment.

New research reveals a dramatic improvement in diagnosing and curing people living with hepatitis C in rural communities using both telemedicine and support from peers with lived experience in drug use.

The study, published in the journal Clinical Infectious Diseases, outlines the results of a randomized controlled trial led by Oregon Health & Science University in seven rural counties in Oregon.

Large language models, a type of AI that analyses text, can predict the results of proposed neuroscience studies more accurately than human experts, finds a new study led by UCL (University College London) researchers.

The findings, published in Nature Human Behaviour, demonstrate that large language models (LLMs) trained on vast datasets of text can distil patterns from scientific literature, enabling them to forecast scientific outcomes with superhuman accuracy.

New research from the Centres for Antimicrobial Optimisation Network (CAMO-Net) at the University of Liverpool has shown that using artificial intelligence (AI) can improve how we treat urinary tract infections (UTIs), and help to address antimicrobial resistance (AMR).

AMR occurs when bacteria, viruses, fungi, and parasites evolve and no longer respond to treatments that were once effective.

Researchers from the National Institutes of Health (NIH) have developed an artificial intelligence (AI) algorithm to help speed up the process of matching potential volunteers to relevant clinical research trials listed on ClinicalTrials.gov. A study published in Nature Communications found that the AI algorithm, called TrialGPT, could successfully identify relevant clinical trials for which a person is eligible and provide a summary that clearly explains how that person meets the criteria for study enrollment.