AI companions are becoming increasingly popular, with millions of users forming deep connections with persona-based chatbots and simulated AI partners. But if you’re considering leaning on an AI companion for emotional support, a study by researchers in the lab of Diyi Yang, an assistant professor in the Computer Science Department at Stanford, suggests you might want to think twice: As reported in Nature Human Behavior, the researchers found that opening up to chatbots about personal issues made some people feel not better but worse.

Artificial intelligence (AI) is becoming increasingly powerful at reading pathology slides. But a new Perspective in Science Bulletin argues that accuracy alone is not enough. For AI to truly help pathologists, it must work safely and transparently in real clinical settings.

The research article discusses how computational pathology is moving from technical innovation toward clinical integration.

A new artificial intelligence (AI) tool developed by La Trobe University researchers could help predict which stage-two bowel cancer patients are at risk of relapse, in an advance that could ultimately lead to thousands getting life-saving treatment earlier.

Published in the journal Gastroenterology, the research documents the creation of SÉMIL (Semantically-Enhanced Multiple Instance Learning),

Biomedical engineers at Duke University have demonstrated a method for systematically developing novel, complex combinations of probiotics and prebiotics to more effectively maintain gut health and treat various gastrointestinal diseases.

By tactically designing experiments and robotically automating thousands of parallel experiments to fill knowledge gaps that could make the model more accurate,

Penn Engineers have developed PeptiVerse, an AI-powered platform that predicts key chemical and biological properties of peptides, the strings of amino acids whose medical potential has been demonstrated by the success of GLP-1 drugs, the widely used weight-loss treatments.

In Nature Communications, the researchers describe how they trained PeptiVerse using a wide range of data sets, allowing it to predict properties that can help determine whether a peptide is worth pursuing as a potential drug,

A UVA Health emergency medicine doctor and colleague at Clemson University have developed a framework to help hospitals integrate artificial intelligence (AI) to not just increase efficiency and cut costs but ensure high-quality patient care remains the top priority.

With the massive potential of artificial intelligence to transform healthcare in the coming years, UVA’s R. Andrew Taylor, MD, MHS, and Clemson’s Arwen B.L. Declan, MD, PhD, created the new framework to ensure healthcare remains “rooted in its ethical obligations”

Researchers have built a tool to uncover hidden issues in the massive datasets used to train medical AI.

The tool searches training data for subtle patterns that could lead AI models to incorrect conclusions, potentially jeopardizing patient care. The work aims to help researchers and regulators make artificial intelligence more reliable and trustworthy for real-world clinical use.