Healthcare AI | Aidoc Always-on AI

Many health systems already have some level of AI in place. But oftentimes, the existing AI tools are fragmented — creating more silos, more vendors, more integration work, and more burden than value. To truly scale clinical AI, health systems need to focus on systemwide standardization, including infrastructure, workflow integration, governance, training, and measurement acr...

In healthcare, using AI solutions can make a life-changing difference for patients. But if clinicians don’t feel comfortable or excited to use the tools, they won’t use them effectively. To get the most value out of clinical AI, clinicians must be able to actively use it in the course of care, trust its output, and act on insights without disrupting their standard workflow.

In many rural communities, seeing a specialist can mean driving hours for an appointment or waiting weeks for care. And as hospitals struggle to recruit clinicians, patients are often left with even fewer options.

The challenge won’t ease anytime soon. The Health Resources and Services Administration reports that thousands of communities across the country are alrea...

New Zealand’s healthcare system faces a challenge familiar to many countries with dispersed populations: hospitals don’t always have a radiologist on site, and the specialists who can interpret complex, time-critical scans are often based hours away. For a patient presenting with a possible stroke, pulmonary embolism, or intracranial hemorrhage in a rural or regional facility...

Healthcare has spent decades improving patient safety, yet diagnostic delays and errors affect hundreds of thousands of patients each year.

More than a decade ago, the National Academy of Medicine identified diagnostic error as a major patien...