Healthcare & Life Sciences
Accelerate medical research and improve treatments without compromising patient privacy
Advancing healthcare requires secure and compliant data collaboration
Sherpa.ai’s Federated Learning platform enables biopharma companies, researchers, and healthcare providers to collaboratively develop and validate new treatments—without ever sharing sensitive patient data.
By bringing privacy-preserving AI to decentralized data sources, Sherpa.ai accelerates medical innovation and helps significantly reduce the time it takes for life-saving therapies to reach the people who need them.
The application of Artificial Intelligence through Sherpa.ai’s Privacy-Preserving platform will allow the prediction algorithm to improve the diagnosis without the need to share any patient data. This platform may enable testing of diagnosis and therapeutics for a group of diseases that are currently without specific treatment options.
Senior Investigator and Acting Chief Neurogenetics branch at NIH
Federated Learning Cases in Healthcare & Life Sciences
Enhanced treatments and diagnostics
Hospitals and research centers can collaboratively train AI models on decentralized medical data— enhancing diagnostic accuracy while fully protecting patient privacy and medical records.
Early Detection of Rare Diseases
Combine distributed knowledge across multiple institutions to uncover patterns hidden in isolated datasets.
Personalized Treatment Plans
Design therapies tailored to each patient while maintaining full data control and regulatory compliance.
Accelerate clinical trials
Accelerate validations and improve accuracy by securely integrating federated data from multiple medical centers.
Advancing the Diagnosis of Collagen VI-Related Dystrophies Through Cross-Institutional Collaboration
NIH (US) and UCL (UK) are leveraging Sherpa.ai’s platform to advance the diagnosis of Collagen VI-related dystrophies (COL6-RD)—without transferring sensitive patient data.
Diagnosing rare diseases with Machine Learning is often limited by scarce and siloed data. Traditional multi-center data aggregation poses significant privacy, regulatory, and logistical challenges. Sherpa.ai’s privacy-preserving solution overcomes these barriers by enabling collaborative training across decentralized datasets without sharing patient data.
By training on a larger and more diverse dataset, the model achieves significantly improved diagnostic accuracy. This marks the first global application of Federated Learning for COL6-RD diagnosis, using collagen VI immunofluorescence microscopy images from patient-derived fibroblast cultures.
