Clinical trials
On-premise AI for site selection and trial recruitment
Collaborate on sensitive data using AI across distributed sources, without moving it.
Better clinical trials for everyone
Leverage AI to identify high-performing trial sites and eligible patients faster—without moving sensitive health data.
Use cases
Patient pre-screening
Accelerate enrolment while protecting privacy
Match patients to trial criteria within hospital systems using federated queries and AI on imaging and EHR data, boosting recruitment speed without sharing raw data.
Protocol design
Design smarter studies grounded in real-world data
Simulate inclusion criteria and optimize endpoints using federated analytics across diverse datasets without ever moving sensitive patient data.
Site feasibility
Assess site readiness instantly and securely
Evaluate potential trial sites’ patient populations and data availability via federated queries that keep all data under local control.
Site selection
Choose the best-performing sites backed by objective evidence
Identify optimal sites based on real, distributed patient and performance data, ensuring every selection decision is data-driven and privacy-preserving.
Imaging and EHR data transfer
Set up a data exchange network to transfer clinical data securely and compliantly
Move imaging and EHR data from sites to sponsors or CROs through Bitfount’s governed data-transfer workflows, ensuring full traceability, encryption, and regulatory compliance at every step.
AI/Model development
Train AI across partners, without sharing data
Build, validate, and deploy machine learning models directly on distributed datasets using federated learning to maintain confidentiality and fairness.
Real world evidence & post-market surveillance
Generate insights from real-world data securely
Conduct longitudinal safety and efficacy studies across multiple data custodians while preserving each organization’s data privacy.
Model evaluation
Test models across diverse datasets, privately
Validate predictive models on external cohorts through federated evaluation to ensure generalizability. No sensitive data ever leaves its source.
Bitfount platform
No-code desktop application
Easy to install on Windows or Mac
Deep imaging capabilities
Analyse OCT, FAF, CF, SLO images and more
Integrate your EMR
Connect your EMR data for deeper analysis
Model agnostic
Use any model provider with Bitfount
Certifiably secure
AI based patient pre-screening finds 64% more eligible patients than EHR search alone
Frequently asked questions
Can Bitfount access my data?
No, Bitfount’s zero-trust architecture ensures Bitfount never receives data or analysis results. Analysis results are transferred between data custodians and data scientists end-to-end encrypted using encryption keys held by each party and not accessible to Bitfount. Read more here.
What will my Information Governance team think of this?
Information Governance and Legal teams love Bitfount since it removes the need for complex Data Sharing Agreements (DSA) and Material Transfer Agreements (MTA), and massively simplifies Data Protection Impact Assessments (DPIA). Bitfount is also fully GDPR, HIPAA and ISO27001 compliant. Visit our online Trust Centre here.
Do I need to be able to code?
No. Our desktop application has been designed to be operated without requiring any coding knowledge. This includes connecting datasets, joining projects, running tasks and accessing analysis results. Data scientists and algorithm developers can choose to interact with Bitfount via our python SDK.
Are there any hardware requirements?
There aren’t any specific requirements, however AI analysis at scale can be compute-intensive and will run more efficiently, especially for image analysis, if you have access to a machine (physical or virtual) with a GPU. Suitable devices include any Apple silicon model, or a Windows or Linux machine with an Nvidia GPU. Not sure if you have the right setup? Reach out to us at support@bitfount.com for guidance.
Any more questions?
Contact our team for tailored guidance or more details about your use case.
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