New statistical models based on physiological data from more than 100 surgeries provide objective, accurate measures of the body’s subconscious perception of pain.

Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.

“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.

Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.

The software tool NeuroTrALE is designed to quickly and efficiently process large amounts of brain imaging data semi-automatically.

The approach can detect anomalies in data recorded over time, without the need for any training.

MIT Concrete Sustainability Hub research presents a streamlined pavement life-cycle assessment tool to enable a large set of stakeholders to conduct environmental analysis of pavements.

Reflecting a “Moore’s Law of aviation,” commercial flight has become roughly twice as safe each decade since the 1960s; Covid-19 added a wrinkle, however.

More efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.

Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.