Clinical trials

On-premise AI for site selection and trial recruitment

Collaborate on sensitive data using AI across distributed sources, without moving it.

Use cases

Patient pre-screening

Patient pre-screening

Accelerate enrolment and reduce screen failures while protecting patient privacy

Match patients to trial criteria within hospital systems using federated queries and AI on imaging and EHR data, boosting recruitment speed and reducing screen failure rates, without sharing raw data.

Patient pre-screening

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.

Patient pre-screening

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.

Patient pre-screening

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.

Patient pre-screening

Pseudonymised data transfer

Set up a data exchange network to transfer imaging and EMR 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.

Patient pre-screening

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.

Patient pre-screening

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.

Patient pre-screening

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

Easy IT integration

Install in seconds via desktop app (available for Windows and Mac) or Python SDK

No-code workflow

Intuitive workflow for use by anyone from physicians to research teams to data scientists

Powerful data governance & IP protection

Built-in governance features ensure you’re always in control of how your data is used and by whom

Deep imaging capabilities

Efficiently analyse 2D or 3D medical images. No de-identification or pseudonymisation required

Integrate your EMR

Connect your Electronic Medical Record (EMR) system for deeper cross-silo analysis

Model agnostic

Use commercial, academic or open-source models. No vendor lock-in

Download

Supported image formats

Supported EHR systems

Supported model frameworks

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.

Still have questions?

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