This paper presents a structural analysis of clinical AI, clinical decision support, human oversight, patient safety, and shared answerability in healthcare systems. It argues that clinical decision support tools are often described as assistive rather than governing, but that in real hospital settings assistance can become default steering when recommendations align with workflow pressure, documentation burden, institutional liability, and time scarcity. In these conditions, clinicians may remain formally responsible while becoming functionally dependent on machine-shaped pathways. The paper uses the Structural Intelligence framework to show how human-in-the-loop arrangements can remain visible while answerability thins in practice, especially when disagreement is costly and revision pathways are weak. It identifies key failure modes in clinical deployment, including oversight theater, burden export, substitute control, and closure failure, and proposes a shared-answerability architecture for clinical AI. The proposed design includes causal anchoring, adversarial mirroring, protected override, consequence-bearing authorization, burden-path audit, and binding revision triggers. A sepsis triage scenario shows how these issues appear in hospital workflow and how safer design would allow contradiction to become correction before harm is externalized. The paper speaks to healthcare AI, responsible AI, medical informatics, digital health governance, human-computer interaction, and technology policy.
Vladisav Jovanovic (Fri,) studied this question.