Accelerate Clinical Trials with Federated AI | Sherpa.ai

At Sherpa.ai, we know the traditional clinical trial model faces a critical bottleneck: data. Valuable patient information is siloed within individual hospitals and across borders, locked down by essential  privacy regulations like HIPAA and GDPR.

This fragmentation limits dataset diversity, introduces bias, and ultimately slows down the path to discovery.

We built our platform to solve this. We empower pharmaceutical companies and research institutions to collaborate on a global scale, training powerful AI models on diverse datasets without ever moving or exposing sensitive patient information.

The Challenge We Solve: Data Access vs. Data Privacy

As leaders in clinical research, you face a constant dilemma. To build robust models that predict treatment efficacy, you need vast and varied data. Yet, sharing this data is a logistical and regulatory minefield.

Centralizing patient records increases security risks and creates compliance challenges that can stop promising research in its tracks. You shouldn't have to choose between innovation and privacy.

Our Solution: A Paradigm Shift in Collaboration

Our Federated AI platform flips the traditional data-sharing model on its head. Instead of bringing your sensitive data to a central server, we send the AI model securely to your data.

Here’s how our platform works:

• Distributed Training: We send a global AI model to each of your participating hospitals or research facilities.

• Local Learning: The model trains exclusively on the local data, safely behind the institution's own firewall. Raw patient data never leaves its source.

• Secure Aggregation: Only anonymized insights and model updates—never the underlying data—are sent back and aggregated to improve the central, global model.

• Iteration: This newly enhanced global model is sent back to the local sites for a continuous cycle of learning and improvement, getting smarter with each step.

Real-World Scenarios Our Platform Enables

This isn't just theory; it's a practical solution for today's most complex research challenges.

Scenario 1: Advancing Rare Disease Research

A pharmaceutical company is developing a treatment for a rare neurological disorder. Data is incredibly scarce, with only a handful of patients at individual research centers in the US, Germany, and Japan. Due to HIPAA, GDPR, and APPI regulations, direct data sharing is impossible.

With our AI Solution fo Healthcare: The AI model trains on patient data inside each of the three centers simultaneously. The anonymized learnings are combined to build a single, robust predictive model for disease progression—a feat no single institution could achieve alone, accelerating the path to a viable treatment.

Scenario 2: Ensuring Drug Efficacy Across Diverse Populations

An oncology drug has proven effective in a trial with a predominantly European cohort. To gain global approval and ensure health equity, the drug's efficacy must be validated across different ethnic groups in Asia and Africa, whose genetic data cannot be moved.

With our AI Solution fo Healthcare: We connect the primary research hospital with partner clinics in Lagos and Seoul. The efficacy model trains locally on diverse genomic and clinical data. The aggregated global model identifies subtle population-specific response markers, strengthening regulatory submissions to the FDA and EMA while paving the way for personalized dosing strategies worldwide.

The Quantifiable Impact: ROI and Performance Metrics

The shift to a federated approach delivers a clear and compelling return on investment compared to traditional centralized AI strategies.

Driving Superior Model Performance

By training on more diverse, real-world data, our federated models consistently outperform those trained at a single institution.

We have seen clients achieve:

• Up to a 25% increase in the predictive accuracy of diagnostic and prognostic models.

• A reduction in demographic and geographic bias by over 30%, leading to more equitable and globally applicable results.

A Clear Return on Investment (ROI)

The primary alternative—centralizing data—is financially prohibitive and slow. Consider a typical multi-site, international AI research project:

• Traditional Centralized AI Approach Costs:

  • Data De-identification & Anonymization: $400,000 - $700,000

  • Secure Cloud Infrastructure & Transfer: ~$1M+ over 3 years

  • International Legal Counsel for Data Use Agreements: $250,000+

  • Total Initial Outlay: Often exceeds $1.5 - $2 Million USD before research even begins.

• The Sherpa.ai Federated Advantage: Our platform eliminates nearly 100% of the costs associated with data transfer and de-identification. This alone can translate into direct savings of over $1 Million USD per project. The legal framework is also simplified, reducing legal expenditure by up to 80%.

More importantly, the timeline is radically accelerated. The legal and data-sharing negotiation phase, which often takes 12-18 months in a centralized model, can be reduced to under 3 months. Accelerating a blockbuster drug's time-to-market by even six months can represent hundreds of millions of dollars in revenue, making the ROI on our platform astronomical.

Built for Global Compliance: Privacy by Design

Our platform isn't just compliant with privacy laws; it was fundamentally designed around them. This Privacy by Design approach provides a universal solution for global collaboration.

GDPR (Europe)

Directly address core GDPR principles. Data never leaves its jurisdiction, satisfying data sovereignty and residency rules for international transfers. The process inherently enforces Data Minimization.

HIPAA (USA)

Protected Health Information (PHI) is never moved, transmitted, or exposed. Our platform operates within a healthcare provider's existing secure environment, and model updates cannot be reverse-engineered to identify any individual patient.

A Universal Framework

Because the foundational data never moves, our platform’s architecture inherently aligns with the core principles of virtually every major data privacy law worldwide, including Brazil's LGPD, Canada's PIPEDA, and Japan's APPI.

We believe the future of medical research is collaborative, secure, and fast. Our platform provides the statistical power of a massive, centralized dataset with the unbreachable security of keeping all data local.

Reduce timelines and costs in your clinical trials. Discover how with our platform.