Current Situation
Medical imaging forms a critical piece of the picture in patient care but costs healthcare billions of dollars each year because of it being overutilized, resource intensive, and highly siloed.
Goals and Objectives
Deploy analytics, AI, and ML that learn to see what clinicians cannot and offer the next best steps toward first-time right diagnoses. Optimize imaging workflow processes such as during imaging triage, documentation and reporting, acquisition, visualization, and annotation to improve productivity, quality, and experiences.
Technology Deployed
- AI-driven advanced analytics platforms, predictive modeling, machine learning, and deep learning algorithms
- Big data imaging sets, cloud, GPUs, SDKs, pretrained models, and annotation tools
- RIS, PACS, VNA, AICA, and enterprise imaging systems
- EHRs, EHR integrations, openAPIs, and interoperability standards (e.g., HL7 FHIR)
Use Case Summary
Imaging analytics, AI, and ML offer a way for imaging to complement and augment talent by becoming more data driven and appropriately used, paving the way toward value-based imaging and away from defensive medicine.