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.
Our work is published in peer-reviewed journals and presented at top AI + medical conferences.
Priya Sharma, Rohan Mehta, Aditya Nair — PrajnixLabs Research Group · Proceedings of AAAI 2024, pp. 1423–1431
Aditya Nair, S. Krishnaswamy, Vikram Patel — PrajnixLabs & AIIMS Delhi · Scientific Reports 14, 18342 (2024)
Rohan Mehta, Anjali Rao — PrajnixLabs · MICCAI 2023 Workshop on Federated Learning in Medical AI
PrajnixLabs Research Team · arXiv:2501.XXXXX (2025)
Our research translates into tangible products that hospitals and diagnostic labs can integrate into existing workflows.
Upload histopathology slide images for automated malignancy scoring, cell morphology analysis, and report generation. Integrates with existing lab LIMS.
Request DemoNLP engine that reads free-text radiology reports and flags high-risk language patterns indicative of early malignancy, routing urgent cases for faster review.
Request DemoREST API for integrating genomic mutation data into risk stratification models. Built for research hospitals and biotech companies running NGS pipelines.
Request AccessWe 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.