Gesund.ai orchestrates the AI as-a-Medical Device lifecycle, providing privacy-centered access to diverse yet standardized medical data sources, and a unique analytical toolbox that fosters clinical validation, regulatory clearance and effective marketing
Model owner shares clinical study with Gesund for curation of appropriate dataset(s), and uploads their model onto Gesunds federated validation platform, which resides on hospital premise or private cloud.
Model runs against a previously unseen validation data set that has been curated on the hospital side.
Model accuracy metrics are produced and displayed on the Gesund platform for further examination with respect to patient characteristics, scenario analyses and stress testing.
The model insights are exported into a report for the model owner to supplement their regulatory submission.
No-hassle model exploration
and validation running models against
real-world data in a secure environment
No more dependency
on software engineers to
containerize or deploy models
Share models
and insights out of the
box with collaborators
Dataset matching
according to case-specific
regulatory demands
Annotation
as-a-service
Assessment of demographic
characteristics for explanatory
purposes
Post-market validation
and evaluation
Update model via
re-training with prospective
studies against standard of care
Identify gaps
in algorithms
To tap proprietary data sources and reader expertise through our platform
Can an automated approach help identify clinically significant biases within machine learning prognostic models?
Of the 1,343 total patients included in the study, 179 (13%) patients died. The final model accuracy on the validation cohort overall was 80% (minority class F1 score=0.39, AUC=0.663). However, using our automated tool there were numerous clinically significant differences identified in model accuracy on different patient subsets. For example, the model was much more accurate for patients requiring the intensive care unit (86% accuracy) but was much worse for other sub cohorts such as current smokers (60% accuracy) and male patients (78% accuracy). Moreover, there were many sub cohorts with insufficient patient data to perform sufficient analysis.
These data demonstrate the high risk for model performance discrepancies on subset of patients with different characteristics. Using a standardized, automated approach for systematic model validation is instrumental in minimizing model biases before implementing a machine learning model in a clinical setting.
We are building a privacy-first MLOps platform for data-driven organizations in healthcare and life sciences. The platform is designed to support the entire lifecycle of machine learning (ML) efforts to accelerate breakthrough medical research and bring clinical-grade ML solutions to market. Our fast-expanding strategic network includes early clinical and technology partners and organizations in the US and Europe.
We are building a privacy-first MLOps platform for data-driven organizations in healthcare and life sciences. The platform is designed to support the entire lifecycle of machine learning (ML) efforts to accelerate breakthrough medical research and bring clinical-grade ML solutions to market. Our fast-expanding strategic network includes early clinical and technology partners and organizations in the US, Israel and Europe.
Gesund.ai is building a privacy-first MLOps platform for data-driven organizations in healthcare and life sciences. The platform is designed to support the entire lifecycle of machine learning (ML) efforts to accelerate breakthrough medical research and bring clinical-grade ML solutions to market. Our fast-expanding strategic network includes early clinical and technology partners and organizations in the US, Israel and Europe.