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GalenosBio
AI Products & Digital Health Suite

Proprietary medical AI, built by clinicians.

From predictive radiomics to automated clinical-trial data management, our software is engineered to clinical-grade standards — explainable, validated, and ready for the realities of care.

Radiomics Intelligence

Predictive imaging biomarkers

A predictive radiomics platform that extracts high-dimensional quantitative signatures from routine medical imaging — surfacing biomarkers that guide stratification and response assessment.

  • Automated feature extraction & harmonization
  • Response-prediction and prognostic modeling
  • Reproducible, auditable analysis pipelines

Clinical Decision Support

Evidence at the point of care

A clinical decision support system co-designed with active oncologists — translating guideline logic and patient data into transparent, explainable recommendations.

  • Guideline-aware, explainable reasoning
  • Real-world clinician workflow integration
  • Continuous validation against outcomes

Clinical Data Fabric

Automated trial data management

An end-to-end medical data management layer that automates capture, curation, and standardization of clinical trial data — CDISC-native from the first datapoint.

  • Automated EDC-to-SDTM transformation
  • Live data-quality & anomaly monitoring
  • Submission-ready ADaM datasets

Medical SaaS Solutions

Tailored clinical software platforms

Purpose-built medical SaaS — designed around your workflows, data standards, and regulatory constraints — so teams capture, manage, and act on clinical information without fighting generic tools.

  • Workflow-native design for clinical operations
  • Secure, role-based access and audit trails
  • Integration with EDC, imaging, and hospital systems

Medical AI Solutions

Bespoke models for your indication

Custom medical AI solutions scoped to your therapeutic area and data — from imaging and genetics to trial intelligence — built with clinical validation and explainability from day one.

  • Indication-specific model development
  • Clinician-in-the-loop validation
  • Deployment paths for research and care settings