The protected health information of deidentified individuals may not be private after UC Berkeley researchers used machine-learning techniques to reidentify the health data of some children and adults. The findings could signal a need for legislation that protects and ensures the privacy of personal health data.

The expectation is that AI will revolutionize healthcare for patients and providers. But before AI’s potential can translate into action, several key questions must first be addressed, a recently published viewpoint argued in JAMA.

AI is expected to have a big impact on the way people gain access to healthcare services, according to the 2018 Health Trends report published by Stanford Medicine.

While most physicians are skeptical AI will fully replace them in the future, many do believe the technology will be able to make prognoses and overtake some administrative tasks, according to a survey of 740 general physicians in the United Kingdom.

The University of Guelph in Ontario, Canada, has launched a new center that aims to address the ethics of AI and build "machines with morals" by ensuring technologies benefit people and minimize harm.

An AI company is giving charities an opportunity to boost their medical research and solve critical challenges with its platform.

The UK government and life sciences industry leaders are investing more than £1 billion to support healthcare innovation and research aimed at using AI for early disease detection.

The National Institutes of Health has awarded a $1.2 million grant for a project aimed at restoring voluntary movement in paralyzed limbs using AI.

The National Institutes of Health (NIH) and tech company NVIDIA are partnering to create AI tools for clinical trials focused on brain and liver cancer.

Insilico Medicine, a Maryland-based AI company, is asking researchers to contribute to its new platform aimed at boosting AI-powered drug discovery.

A Stanford University research team used machine learning to quickly and accurately diagnose autism in children through short home videos, according to a research article published in PLOS Medicine.

Using a deep-learning model, a University of Massachusetts Lowell research team was able to significantly improve the extraction of adverse drug events (ADEs) from electronic health records (EHRs).