AI/ML Models for early RISK prediction-FDA Exempt, Jan 2026
Advancing Predictive Health
The Role of AI-Enhanced Blood Marker Analysis in Early Detection of Chronic Diseases and Cancer
AI Prediction Models
AI Prediction Models driven by routine blood markers to predict risks for life-threatening chronic diseases including cancers.
80+ AI/ML Models for Life-Threatening Diseases in Development
We have developed a portfolio of patent-pending AI/ML models designed to predict the risk of life-threatening conditions, including cancers as well as chronic and acute diseases. These models leverage routine blood markers only, enabling cost-effective, efficient, and highly scalable screening across diverse populations. This approach makes early detection more accessible, equitable, and impactful on a global scale.
Model Dashboard
The models provide...
- The risk scores
- Confidence of the model
- The probability of the condition in a given patient compared to the prevalence rate
- Relative blood markers of importance
- True / False positive and negative rates
- Additional specific lab tests needed to confirm/ rule out the risk.
Evidence Based Prediction
Our models are grounded in evidence-based research, with scientific publications linking key blood markers to the risk of specific life-threatening conditions. Each model explains the detailed biochemical mechanisms of action, strengthening confidence in its predictions. This approach not only enhances clinical trust but also equips clinicians to monitor patients effectively and evaluate appropriate intervention strategies.
Lifestyle Modification
The report includes a model-aligned, mechanism-driven lifestyle plan tailored to the above ML framework outputs for lung cancer risk stratification (as in this case study: elevated monocytes, BUN, high-normal calcium, high-normal platelets).
This translates each biomarker signal into targeted vegetarian interventions consistent with the system’s interpretability layer.
Population Screening Cascade
Our models enable a comprehensive, safe, and cost-effective approach to population-level screening for life-threatening diseases with low baseline prevalence. Leveraging a Negative Predictive Value (NPV) of over 99%, these models accurately identify and stratify sub-populations with significantly higher prevalence rates. This enriched group can then undergo advanced testing with specialized blood markers, followed by imaging or biopsy when clinically indicated. This cascade ensures efficient resource utilization while maximizing early detection and patient outcomes.