A Human-Centric Deterministic-Kernel Probabilistic-Shell Architecture with Invariants that Confine Clinical AI Decision Authority

Medical artificial intelligence faces a structural tension: capability arises from probabilistic models, whereas safety requires guarantees those models cannot give. We implement and verify an architecture in which the probabilistic component holds no execution authority. A deterministic kernel is the sole output path and adjudicates every candidate through one fixed safety envelope. A human-centric core turns the patient's own account into decision weights, bounded so that no weight can widen the boundary. Verification runs along four lines: invariant, property-based, mutation, and adversarial. The formal lines passed but shared one blind spot, which a red team of 276,727 blind attacks from two vendors exposed: unenumerated input silently reaching the most permissive branch. We report this defect class, its remedy, and re-verification, including against the deployed system. Verification is architectural and does not establish clinical efficacy.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-27
DOI
https://doi.org/10.5281/zenodo.22114583
Citations
2
Primary Topic
Adversarial Robustness in Machine Learning
Type
preprint
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preprint

A Human-Centric Deterministic-Kernel Probabilistic-Shell Architecture with Invariants that Confine Clinical AI Decision Authority

Lu-An Chiu
2 citations
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
preprint

A Human-Centric Deterministic-Kernel Probabilistic-Shell Architecture with Invariants that Confine Clinical AI Decision Authority

Lu-An Chiu
preprint en
2 citations

Abstract

Medical artificial intelligence faces a structural tension: capability arises from probabilistic models, whereas safety requires guarantees those models cannot give. We implement and verify an architecture in which the probabilistic component holds no execution authority. A deterministic kernel is the sole output path and adjudicates every candidate through one fixed safety envelope. A human-centric core turns the patient's own account into decision weights, bounded so that no weight can widen the boundary. Verification runs along four lines: invariant, property-based, mutation, and adversarial. The formal lines passed but shared one blind spot, which a red team of 276,727 blind attacks from two vendors exposed: unenumerated input silently reaching the most permissive branch. We report this defect class, its remedy, and re-verification, including against the deployed system. Verification is architectural and does not establish clinical efficacy.

Zenodo (CERN European Organization for Nuclear Research)
Tainan University of Technology (TW)
Peace, Justice and strong institutions
Adversarial Robustness in Machine Learning
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