Enlitic focuses the power of artificial intelligence into data management applications, enabling effective administration, processing, and sharing of medical imaging data throughout the healthcare enterprise. The Enlitic framework standardizes, protects, integrates, and analyzes data to create the foundation of a real-world evidence database that improves clinical workflows, increases efficiencies, and expands capacity.
Enlitic is focused on developing tools that solve longstanding challenges in radiology rather than developing point solutions that exacerbate the issues radiology faces. Enlitic provides a solid plan that addresses data inconsistencies and risk associated with moving studies containing PHI and is focused on using AI for the medical imaging layer of a real-world evidence (RWE), providing healthcare organizations with a stepwise approach to operationalizing their AI.
An inspired and committed loyalty to our clients, our partners, and the betterment of patient care.
A blend of knowledgeable foresight and experienced insight that enriches our understanding and informs our solutions.
A true passion that empowers the precision of our products, the collaboration of our approach, and the fervent culture of our team.
A goal-oriented approach that we use to develop purposeful innovations that propel our industry and humanity forward.
No, Enlitic is a data management company that uses AI technologies, Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV). These advanced technologies allow Enlitic’s products to work without the use of mapping or other manual techniques that are common in the industry.
Teleradiology, modality and PACS distributors, hospitals, and OEM vendors are Enlitic’s current customer base. The IT department, along with radiologists and executive decision makers are the individuals that have contributed to the purchasing decision.
Enlitic is unique in the data management space. We do similar things as a handful of organizations, but we do it using the advanced technologies of AI resulting in a list of downstream benefits others do not have. PACS vendors attempt to standardize data through mapping which eventually breaks. Companies that provide standardized data for research do so for the single purpose of research, while the rest of the enterprise fails to recognize the benefits from the improved data quality. Other companies deidentify the data but lack the data standardization capabilities making the deidentified data difficult to search and pull larger cohorts together.