Meta-predicates enforce evidence rules in clinical AI before deployment
A framework using domain-specific languages and epistemological type systems validates that clinical decision logic uses appropriate evidence sources, not just accurate predictions.
Source: arxiv/cs.AI · Michael Bouzinier, Sergey Trifonov, Michael Chumack, Eugenia Lvova, Dmitry Etin · open original ↗
Meta-predicates constrain what evidence types clinical AI rules may use, enabling pre-deployment validation of epistemological soundness.
- — Meta-predicates assert constraints on evidence types before rules execute, catching errors early.
- — Epistemological type system classifies evidence by purpose, domain, scale, and acquisition method.
- — Decision trees reformulated as unate cascades produce per-variant audit trails showing which rule fired.
- — Approach complements post-hoc explainability (LIME, SHAP) with preventive validation.
- — Demonstrated on 5.6M genetic variants; generalizes to any auditable decision domain.
- — Satisfies regulatory requirements for auditability under EU AI Act and FDA guidance.
- — Works with both human-written and AI-generated rules in AnFiSA platform.
Astrobobo tool mapping
- Knowledge Capture Record the epistemological constraints for your domain: what evidence types are permissible for diagnosis vs. prognosis vs. treatment selection. Store as structured metadata.
- Focus Brief Summarize your regulatory obligations (EU AI Act, FDA guidance, local standards) and map them to evidence constraints. Use this as a checklist during rule review.
- Audit Log When deploying a new clinical rule, log which evidence types it uses and which meta-predicates validated it. This becomes your compliance record.
Frequently asked
- A meta-predicate is a rule about rules. It asserts constraints on what types of evidence a clinical decision rule is allowed to use. For example, a meta-predicate might say 'diagnostic rules must use validated biomarkers, not anecdotal reports.' Meta-predicates catch epistemological errors before a system goes live, complementing post-hoc explanation tools.
cite ▸
APA
Michael Bouzinier, Sergey Trifonov, Michael Chumack, Eugenia Lvova, Dmitry Etin. (2026, April 26). Meta-predicates enforce evidence rules in clinical AI before deployment. Astrobobo Content Engine (rewrite of arxiv/cs.AI). https://astrobobo-content-engine.vercel.app/article/meta-predicates-enforce-evidence-rules-in-clinical-ai-before-deployment-a373b4
MLA
Michael Bouzinier, Sergey Trifonov, Michael Chumack, Eugenia Lvova, Dmitry Etin. "Meta-predicates enforce evidence rules in clinical AI before deployment." Astrobobo Content Engine, 26 Apr 2026, https://astrobobo-content-engine.vercel.app/article/meta-predicates-enforce-evidence-rules-in-clinical-ai-before-deployment-a373b4. Based on "arxiv/cs.AI", https://arxiv.org/abs/2604.21263.
BibTeX
@misc{astrobobo_meta-predicates-enforce-evidence-rules-in-clinical-ai-before-deployment-a373b4_2026,
author = {Michael Bouzinier, Sergey Trifonov, Michael Chumack, Eugenia Lvova, Dmitry Etin},
title = {Meta-predicates enforce evidence rules in clinical AI before deployment},
year = {2026},
url = {https://astrobobo-content-engine.vercel.app/article/meta-predicates-enforce-evidence-rules-in-clinical-ai-before-deployment-a373b4},
note = {Astrobobo rewrite of arxiv/cs.AI, https://arxiv.org/abs/2604.21263},
}