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 AI algorithms that can learn to see what clinicians cannot and offer next best steps toward first-time-right diagnoses.
- Optimize imaging workflow processes such as patient triage, reporting, and image annotation to improve productivity, quality, and experiences.
- 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
Artificial intelligence offers a way for imaging to become more data driven and appropriately used in a manner that paves the way toward value-based imaging and away from defensive medicine.