The Curie platform is designed to leverage AI in radiology to reclaim staff time and lower costs by eliminating inefficiencies in workflows, solving capacity challenges, and ensuring medical information is more useful and more effective for patient care. The Curie platform utilizes human, artificial, and real-world intelligence to create an evidence-based solution that makes healthcare data more collaborative, relevant and enlightening for medical professionals.
We believe that artificial intelligence in healthcare is nothing without care. That’s why since 2014, we’ve worked to evolve healthcare processes that affect clinicians and patients by creating better medical AI software.
We take care in our innovations and develop workflow and healthcare data solutions that enable physicians to focus on patients.
Solve decades old challenges using AI powered healthcare data standardization
Build the foundation of your enterprise AI strategy by enabling the fundamental building blocks
Deploy AI point solutions quickly, effortlessly, and cost effectively through a platform approach
Solve workflow challenges, eliminate redundant tasks, and improve reporting efficiencies while maximizing the promises PACS offers.
Improve workflow orchestration and reduce time spent on nan-value tasks enabling more focus on what matters.
Build out your enterprise AI strategy by solving the major challenges that prevent success: interoperability, integration and capacity.
Two years ago, the leaders at Texas University Health System embarked on a project to move all inpatient imaging to a new enterprise imaging platform to consolidate systems and improve operational efficiencies. The new system provides new radiology tools for 3D rendering and registration, improved integration with the dictation system, and has configurable display hanging protocols which could boost productivity.
Many facilities are finding it frustrating trying to deploy their Artificial Intelligence algorithms. In many cases it is taking many months, countless staff and resources and the results are weak. The efforts result in a single point solution focusing on one specific pathology being implemented and the process begins again. AI faces many obstacles in recognizing its true potential, and understanding these top 10 challenges of deploying clinical AI will help organizations be more successful.