Healthcare & Life Sciences
Sherpa.ai is the first SaaS platform for privacy-preserving AI in healthcare, powered by Federated Learning.
Unlock the full value of clinical and real-world data by enabling secure model training and real-time inference across hospitals, research centers, and healthcare partners, while keeping patient data protected and fully compliant with regulations such as GDPR and HIPAA.
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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.
With the help of Sherpa, the world leader in Artificial Intelligence services, we have developed a platform that is capable of predicting the future needs of Intensive Care Units, among other needs. It has helped us to prepare the necessary resources at all times to cover all needs we may have.
Planning Director of the Department of Health
A global challenge: diagnosing pediatric pneumonia without sharing data
This paper presents a groundbreaking Federated Learning approach that transforms how pediatric pneumonia is detected across globally distributed healthcare systems. Leveraging the Sherpa.ai FL platform, it enables hospitals, and research centers to jointly train advanced AI models on chest imaging without transferring or exposing sensitive patient information. The framework achieves a significant improvement in diagnostic accuracy while maintaining compliance with privacy regulations. This innovation marks a major step toward more accurate, equitable, and scalable pediatric care making it possible to deliver earlier, data-driven pneumonia diagnosis for children worldwide, without compromising their privacy.
Reducing by 99% the Communications of a Single Training
This paper presents a novel Federated Learning framework that enables secure, collaborative model training across multiple organizations without sharing raw data. By optimizing communication efficiency, it reduces network exchanges by up to 99%, dramatically enhancing data privacy and security. The proposed architecture improves scalability, lowers energy consumption, and achieves state-of-the-art performance across real-world applications in healthcare, finance, manufacturing, cybersecurity, aerospace and defense.
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.
Private AI in Healthcare: Towards a More Collaborative and Secure Medicine
The digital revolution has transformed nearly every industry, but few have experienced a potential as profound and consequential as healthcare. Artificial Intelligence (AI) is redefining diagnosis, disease prediction, and personalized medicine.
Medical information is highly sensitive and protected by strict regulations—such as the General Data Protection Regulation (GDPR) in Europe or HIPAA in the United States, as well as emerging laws like the EU AI Act.
Transforming the Healthcare Industry with Sherpa.ai’s Federated Learning Platform
The healthcare industry is undergoing a digital revolution. AI promises major advances in diagnostics, personalized medicine, resource optimization, and medical research. However healthcare data is extremely sensitive and heavily regulated.
In this context, Federated Learning emerges as a critical technological solution. It enables the training of AI models without centralizing data, preserving patient privacy and respecting each institution’s data sovereignty.
