A machine-learning algorithm has surpassed four commonly used methods for catching sepsis early in hospital patients, giving clinicians up to 48 hours to intervene before the condition has a chance to begin turning dangerous.  

Healthcare technology moves at lightning speed, with AI and machine learning at the forefront of innovation. Right alongside these new discoveries is blockchain technology, which was popularized through the rise of cryptocurrency, and is seeing its own emergence in healthcare.

If AI is to find a foothold across the worldwide healthcare ecosystem, it will need to rest on research into how it may affect the emerging realm of participatory medicine.

Safeway stores in Arizona are opening health clinics that combine AI, augmented reality and telemedicine to offer grocery shoppers a convenient way to be seen for illnesses and injuries.  

Stanford researchers have demonstrated a way to remotely diagnose autism in children in Bangladesh.

Using deep learning to tease out factors indicative of age-related variability in the way toddlers gaze at visual stimuli, researchers at the University of Minnesota have shown that the technology can accurately distinguish 18-month-olds from 30-month-olds.

A deep-learning algorithm trained entirely on open-source images has outperformed 136 of 157 dermatologists at classifying melanoma, according to a study running in the May edition of the European Journal of Cancer.

Working with SRI International, the California tech lab that gave the world Siri, mental-health specialists at NYU Langone have developed an AI-based tool that uses voice analysis to diagnose posttraumatic stress disorder—potentially via telemedicine.