Deterministic AI Governance Frameworks for High-Consequence Healthcare Systems
Authors: Elena Rostova, Dr. Miriam Chen · SUPENTIS Research Centre
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Abstract
A formal framework for auditing, constraining, and logging generative and predictive AI models operating within clinical decision-support environments.
Methodology
Audit logs evaluated against Medleep clinical workflows across 12 medical disciplines.
Key Empirical Findings
- Deterministic output boundaries prevent non-deterministic model hallucinations in clinical summaries.
- Complete provenance tracking for every AI-generated suggestion presented to clinical staff.
1. Clinical Safety Principles
AI in healthcare must operate strictly as an audited advisor. Every model inference must be logged alongside input parameters, model version hashes, and human clinician validation decisions.
How to Cite
Elena Rostova, Dr. Miriam Chen (2026). "Deterministic AI Governance Frameworks for High-Consequence Healthcare Systems". SUPENTIS Research Series, Technical Report 2026-SR-AI-G.