AI for Medicine
Medical Diagnostics
Traditional machine learning often struggles with limited or incomplete medical data. Our quantum-inspired approach excels at identifying patterns in sparse, complex datasets, providing more accurate diagnoses that adapt to patient variability.
- Detect biomarkers
- Process incomplete or degraded medical images
- Analyze multimodal clinical data efficiently
- Reduce computational resources needed for complex diagnostics
Research: QCML in Circulating Tumor Cell Analysis
Our novel machine learning paradigm outperforms traditional methods for detecting morphology-predicted large-scale transitions in circulating tumor cells using quantum cognition. Our technology excels at finding patterns in complex morphological data.
Breast Cancer Prediction
Our QCML technology achieved superior results in predicting ER+/- tumors for breast cancer patients using only 70 patients and 129 features, outperforming traditional analysis with the same limited dataset.
UTI Diagnosis Enhancement
With urinalysis data from 1,436 patients, our QCML model achieved a precision of 0.64 and AUC of 0.84, outperforming Logistic Regression, Random Forest, XGBoost, and Neural Networks in UTI prediction.
Missing Data Processing
Our QCML Autoencoder can process images with up to 90% of pixels randomly masked, still delivering accurate diagnostics when fed to a neural network - a capability especially valuable in medical imaging.
Applications: Medical Testing
QCML requires fewer computing resources and smaller datasets than traditional approaches while delivering better results. This makes our technology cost-effective for medical applications with limited patient data.
As noted by a collaborating medical testing company:
"Your analysis was more performant than ours. ...The features that your model weighted as more relevant to the prediction made real biological sense."