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AI-based GPT to offer real-time validated health content to counter misinformation

Healthcare professionals, marketers and institutions spend nearly 40 per cent of their time creating and publishing content, which diverts resources from patient care, research and innovation.

The role of NHS Big Data and AI technology in unlocking the future of lung and heart disease treatment

The Government's recently launched AI Action Plan, focusing on creating a National Data Library, marks a pivotal moment for the use of AI in healthcare. It has the potential to unlock unprecedented opportunities for medical research, drug development, and ultimately new treatment options that will benefit patients.

I tested the future of AI image generation. It's astoundingly fast.

Experts at MIT and Nvidia created a hybrid approach for AI image generation that takes far less computation resources while retaining high visual details.

Deep-learning system uses smartphone camera for heart rate monitoring

A team of medical researchers and engineers at Google Research has developed a way to use the front-facing camera on a smartphone to monitor a patient's heart rate. The team has published a paper on the technology on the arXiv preprint server.

Meet Siddharth Nandyala, the teen using AI to detect heart issues in seconds

Siddharth Nandyala is the creator of CircadiaV, an innovative AI-powered application that can detect heart diseases within seven seconds using smartphone-based heart sound recordings.

SSA has a special program for people with hearing problems

The Social Security Administration (SSA) is set to launch an innovative AI-driven program aimed at improving services for individuals with hearing impairments, promising significant cost savings and enhanced efficiency.

Health Care's First AI Registry Is Coming Soon

The Coalition for Health AI is launching a new registry for model cards, or AI "nutrition labels," from across the industry.

Advancing Cardiovascular Care Through Machine Learning Innovations

In this digital era, machine learning transforms cardiovascular disease (CVD) management by enhancing prediction accuracy, adapting to evolving medical data, and optimizing treatment. Vikas Nelamangala's research explores innovations in temporal data simulation and drift-resilient models,