Providers are breaking down data silos and improving interoperability to open up pathways for critical insights.

Facial emotion recognition could have broad applications across health care, education, marketing, transportation, and ...

Data engine creation is a critical yet often overlooked component of healthcare AI, and Patil has designed systems that ...

Funded by a one-year, $500,000 grant from the National Institutes of Health (NIH)’s AIM AHEAD program, the researchers are ...

ML.NET is a cross-platform open-source machine learning (ML) framework for .NET. ML.NET allows developers to easily build, train, deploy, and consume custom models in their .NET applications without ...

Addressing patients' health-related social needs such as housing instability, food insecurity, transportation barriers and ...

Artificial Intelligence (AI) or ‘man and machine at work’ system will become the future of mankind; the norm, not an ...

The FDA’s framework for AI regulation, while robust for premarket evaluation, would benefit from more specific mechanisms for continuous monitoring of AI performance in diverse real-world settings.

AI accelerators are specialized hardware that are designed to enhance the performance of AI and ML applications.

Adding innovations at every turn, artificial intelligence, machine learning, and the Internet of Things are transforming ...

Quantum researchers from CSIRO, Australia's national science agency, have demonstrated the potential for quantum computing to ...

The technology was announced at the JP Morgan Healthcare Conference in San Francisco. In this project, the stakeholders aim to use a human reference genome to combine with patient data in order to try ...