A new study from Washington University School of Medicine in St. Louis describes an innovative method of analyzing mammograms that significantly improves the accuracy of predicting the risk of breast cancer development over the following five years. Using up to three years of previous mammograms, the new method identified individuals at high risk of developing breast cancer 2.3 times more accurately than the standard method, which is based on questionnaires assessing clinical risk factors alone, such as age, race and family history of breast cancer.

Brian Hie runs the Laboratory of Evolutionary Design at Stanford, where he works at the crossroads of artificial intelligence and biology. Not long ago, Hie pondered a provocative question: If a tool like ChatGPT can write original sentences based on patterns found in massive collections of previously written words, what happens if we replace written words with genetic code?

Earlier research showed that primary care clinicians using AI-ECG tools identified more unknown cases of a weak heart pump, also called low ejection fraction, than without AI. New study findings published in Mayo Clinic Proceedings: Digital Health suggest that this type of screening is also cost-effective in the long term, especially in outpatient settings.

Incremental drops in heart function are treatable with medication but can be hard to spot.

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.