Bridging computation and defensible scientific evidence.
Founded by Dr. Patrick R. Hogan, DHA, Celbridge Science productizes battle-tested governance frameworks honed across complex healthcare portfolios and federal translational programs.
Our Mission
Our mission is to establish rigorous, repeatable, and verifiable evidence standards for computational intelligence and emerging methods in life sciences and healthcare.
Operated, then Productized
Designed and ran enterprise AI governance across an active 180-tool portfolio — managing intake, risk tiering, evidence standards, deployment gates, continuous monitoring, and retirement.
Federal Translational Science
Performer on the NIH Common Fund Complement-ARIE program, providing translational readiness assessment and methodological support within the NAMs Reduction to Practice Challenge.
Published Method
Author of two books and creator of the published seven-condition non-compensatory governance framework for predictive risk, plus active research on translational readiness.
Regulatory & Clinical Fluency
FDA risk-based AI credibility framework, FDA–EMA joint principles, DDT qualification, NIST AI RMF, ISO/IEC 42001 & 23894, OECD GD 34, ONC HTI-1, and Joint Commission standards.
Patrick R. Hogan, DHA
Dr. Hogan has designed and operated enterprise AI governance across complex healthcare portfolios and served as a performer on the NIH Common Fund Complement-ARIE program, providing translational readiness assessment and methodological support within the NAMs Reduction to Practice Challenge. Author of two books on risk governance and creator of the published seven-condition non-compensatory evidence framework.
NIH program experience informs Celbridge’s methodological expertise; it does not imply NIH endorsement of Celbridge products or services.
Global Regulatory Framework Harmonization
The Celbridge evidence model directly satisfies and unifies the leading international AI credibility and computational qualification mandates.
Risk-Based AI/ML Credibility
Context of Use boundary validation, model risk tiering, and pre-determined change control plans (PCCP).
Joint AI Guiding Principles
Transparency, human-in-the-loop clinical oversight, and multi-site generalizability standards.
DDT Qualification Program
Defensible surrogate endpoint qualification dossiers for computational and in vitro biomarkers.
AI Risk Management Framework 1.0
Govern, Map, Measure, and Manage functions mapped across all 7 evidence lineage objects.
42001 & 23894 AI Governance
Enterprise AI management system certification, lifecycle risk assessment, and continuous monitoring.
HTI-1 Decision Support Rule
Predictive decision support intervention (DSI) algorithm transparency and source attribute disclosure.