RWD Curation™

Real World Imaging™ Data for Life Sciences and AI

Life Image specializes in ‘living’ data sets of novel imaging that’s linkable to other clinical information. We are the only company on the market today to bring large, heterogeneous, de-identified and linked imaging data sets integrated to longitudinal clinical outcomes to support your real-world evidence programs or for AI software creation, improvement, validation, or maintenance efforts.

Use Cases

Longitudinal data to identify markers of disease prior to diagnosis and progression during treatment.
Supply machine learning teams with ‘control’ data from Life Image versus ‘study’ data from a clinical trial or internal data source,
Research into identification of novel imaging biomarkers for a rare or newly identified disease subtype.
Validate AI models on real world data that have been built on small or narrow data sets.
Design a new AI algorithm intended for deployment to radiology teams within the Life Image HIE installed network or other imaging archive such as a VNA, PACS or EHR.
Support real world evidence (RWE) teams with a novel source of data to support new approaches or techniques.

Imaging is a Clinically Valuable Dataset

The Real World Imaging Database is made up of:

25+

anatomical locations

200 M+

images

700,000+

studies and reports

80+

modalities

Download a Sample File Visit the RWE Learning Center

RWIᵀᴹ Solutions Backed by Experience

Founded in 2008, Life Image is a market leader in solving the many technical, process and structural barriers that have effectively segregated imaging data into unconnected data silos. Rich diagnostic and outcomes data has historically been challenging to access. Important clinical data, such as imaging, is locked in data silos and difficult to incorporate with other sources of data. Sources for demographically diverse, heterogeneous data to support clinical trials, drug development and life science research are limited. And, there is bias and inconsistency in structural data, currently used and proven effective in controlled trials but when used in real world, diverse populations, issues are discovered and patients are harmed.

5 reasons to implement RWIᵀᴹ into RWD curated research programs:

Expand research sources for better precision to accelerate drug development, post-market safety, effectiveness, and label expansion.
Keep patient information protected with de-identification services that can be applied at scale with a high degree of accuracy.
Reduce costs and get drugs to market faster to save lives.
Transform the way you identify potential patient cohorts for clinical trials.
Eliminate manual efforts and reduce costs for trial sponsors.