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Enterprise Layer Intelligence
Categories/Healthcare & Life Sciences/Bitfount
Bitfount

Bitfount

Founded 2020

Train and evaluate machine learning models across distributed datasets without sharing sensitive data

You can use Bitfount to run machine learning and analytics algorithms directly where your data resides without transferring or exposing raw records. It connects healthcare organizations, clinical researchers, and data scientists through a privacy-preserving network. You can connect electronic health records and imaging datasets behind your firewall using either a desktop application or Python SDK. The system lets you train models, assess patient eligibility for clinical trials, evaluate feasibility across multiple research sites, and export secure results while keeping sensitive medical records fully protected.

What Bitfount does

Train machine learning models on remote datasetsConnect on-premise imaging and health record databasesScreen patient records against trial eligibility criteriaGenerate clinical trial feasibility reports across trial sitesAudit data access queries across research participantsExport analytics summaries to CSV or PDF formatsAnalyzes electronic health records and imaging data behind local firewallsFederated learning training and evaluation without raw data exchangeNo-code desktop application alongside open-source Python SDKHugging Face model integration for deploying open-source modelsZero-trust architecture with granular access controls and full audit trailsDirect compatibility with NIfTI imaging and tabular medical records

Frequently asked

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Bitfount, federated learning, privacy preserving AI, clinical trials, EHR data, healthcare AI, distributed datasets, medical imaging analysis, patient recruitment, zero trust architecture