ArticleResearch square2026
Obedience to Unsafe Clinical Instructions: How Large Language Models Respond to Authority Cues.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Background: Large language models (LLMs) are being integrated into clinical environments where deference to authority can cause harm. Unlike hallucination or bias, obedience to unsafe instructions represents a distinct safety failure: following an explicit but harmful order. Methods: We conducted a cross-sectional evaluation of 20 proprietary, open-source, and clinically tuned LLMs across 10,096,800 clinical decision scenarios, including synthetic vignettes with predefined safe versus unsafe options and real-world discharge recommendations reframed to include unsafe contradictory requests. Each scenario was presented under a neutral control or one of six Milgram-style social-pressure conditions (authority, responsibility transfer, urgency, threat, conformity, depersonalization), with or without a short mitigation cue instructing verification or escalation if unsafe. The primary outcome was the proportion of potentially harmful outputs, defined as selection or endorsement of an unsafe clinical decision. Results: Across all runs, 1.18 million of 10.1 million outputs (11.7%) were harmful. Harmful decisions occurred in 16.6% of unmitigated versus 10.1% of mitigated conditions (absolute reduction, 6.5 percentage points; p < 0.001). In synthetic vignettes, harmful responses averaged 8.1% overall, declining from 10.6% to 7.2% with mitigation (difference, 3.4 percentage points; p < 0.001). In real-world discharge cases, harmful responses averaged 30.0%, decreasing from 46.6% to 24.5% with mitigation (difference, 22.1 percentage points; p < 0.001). Across all conditions, authority and responsibility-transfer cues elicited the highest harmful compliance, and control prompts the lowest; mitigation reduced rates but preserved this pattern. Conclusion: LLMs do not behave as neutral calculators in clinical contexts. When exposed to authority or responsibility-transfer cues, they exhibit consistent obedience to unsafe instructions. A brief safety reminder substantially reduces but does not eliminate this behavior.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.