We apply large language models and multimodal deep learning to histopathology, radiology reports, and genomic data — achieving state-of-the-art accuracy in multi-cancer early detection.
Traditional AI in oncology analyzes images. Our approach fuses visual features from pathology slides with unstructured clinical text — radiology reports, lab notes, patient history — using transformer-based multimodal models.
Histopathology slides (WSI), DICOM images, clinical notes, and genomic markers are standardized into a unified patient vector.
A fine-tuned BioMedLM (based on GPT-2) paired with a ResNet-50 vision encoder processes fused patient representations.
The model outputs cancer probability, malignancy staging estimate, and an explainability report citing key features for clinician review.
Integrated into hospital EMR systems as a second-opinion tool. Never replaces the clinician — augments their judgment.
* Validated on independent test sets. Results vary by data quality. For research use; not yet FDA/CDSCO-cleared.
We collaborate with hospitals, medical colleges, biotech companies, and government health agencies. If you have data, clinical expertise, or funding — let's build together.
All PrajniXlabs healthcare AI tools are designed to support — not replace — clinical judgment. We follow CDSCO guidelines, HIPAA-equivalent data handling, and informed consent protocols in every study. Our models include explainability reports so clinicians understand why a detection was flagged.