Cancer biology cannot be explained by a single biomarker. Harae Dx’s AI learns the relationships between five complementary biological pathways to generate an explainable cancer risk prediction.
A multi-layered, explainable AI architecture that decodes protein relationships to deliver accurate, early cancer risk prediction.
Integrated clinical context — history, demographics, imaging, and patient risk factors — enhances biological interpretation.
Quantitative measurement of five complementary protein biomarkers from a simple blood sample, spanning cell signaling, matrix remodeling, tumor-associated, inflammatory, and angiogenic pathways.
A biology-guided knowledge base captures the pathways and relationships that drive cancer development and progression.
A proprietary AI engine learns complex biomarker relationships and patterns using advanced machine learning and explainable AI.
AI insights are translated into risk stratification with calibrated probabilities and recommendations.
Clinicians receive clear, actionable results in under 30 minutes to guide timely decisions.
A continuously learning system that improves with every patient, in every setting, across every geography.
Across hospitals, clinics, and countries.
Biomarker data linked with clinical outcomes, demographics, imaging, and follow-up.
The AI learns biomarker interactions and disease signatures, and improves explainability.
Higher sensitivity, higher specificity, earlier detection, and explainable recommendations.
More hospitals, more clinicians, more trust, more usage.
New populations, geographies, cancer types, and biomarkers.
Before any human sample was tested, the underlying AI risk model was built and stress-tested computationally, then confirmed against real biological samples in independent retrospective studies.
Reflects the current five-biomarker AI model.
In silico patient profiles used to train the model
In silico patient profiles used to test the model
Sensitivity and specificity achieved in silico
The model was then checked against 350+ actual patient samples obtained from research repositories in the US and Europe, ahead of the independent retrospective studies below.
Conducted using the original four-biomarker panel.
| Study | Plasma samples | Sensitivity | Specificity |
|---|---|---|---|
| Independent research institution (U.S.) | 120 (100 with cancer, 20 controls) | 91.67% | 100% |
| External clinical validation partner | 92 (68 with cancer, 24 controls) | 97.06% | 100% |
Beyond the two studies above, the same four-biomarker panel was further validated across 7 independent lab studies using retrospective plasma samples — ranging from 56 to 308 samples each, with performance consistent across sample sets and independent of patient ethnicity.
| Study | Sample size | Sensitivity | Specificity | Accuracy |
|---|---|---|---|---|
| Study 3 | 75 (44 with cancer, 31 controls) | 92.11% | 85.71% | 89.39% |
| Study 4 | 92 (54 with cancer, 38 controls) | 82.50% | 80.00% | 81.43% |
| Study 5 | 120 (97 with cancer, 23 controls) | 92.50% | 85.71% | 91.95% |
| Study 6 | 56 (34 with cancer, 22 controls) | 85.19% | 81.25% | 83.72% |
| Study 7 | 82 (41 with cancer, 41 controls) | 94.74% | 100.00% | 97.40% |
| Study 8 | 110 (52 with cancer, 58 controls) | 100.00% | 100.00% | 100.00% |
| Study 9 | 308 (140 with cancer, 168 controls) | 89.92% | 94.41% | 92.37% |
Across these 7 studies: average sensitivity and specificity of approximately 90%, average positive predictive value of 93.50%, average negative predictive value of 82.82%, and average accuracy of 90.89%.
Harae Dx has received FDA Breakthrough Device Designation, supporting an expedited path toward regulatory review.
Fast-track clinical trial pathway discussions underway in India.