Evidence map›Paper›PMID 41890849›Full record

ArticleResearch square2026

Obedience to Unsafe Clinical Instructions: How Large Language Models Respond to Authority Cues.

Mahmud Omar, Reem Agbareia, Jolion McGreevy, Alon Gorenshtein, Alexander Charney, Ankit Sakhuja, Benjamin S Glicksberg, Girish Nadkarni, Eyal Klang

Abstract readPreprint
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mahmud OmarIcahn School of Medicine at Mount Sinai.ORCID 0009-0001-0438-0827
Reem AgbareiaIcahn School of Medicine at Mount Sinai.
Jolion McGreevyIcahn School of Medicine at Mount Sinai.
Alon GorenshteinIcahn School of Medicine at Mount Sinai.
Alexander CharneyIcahn School of Medicine at Mount Sinai.ORCID 0000-0001-8135-6858
Ankit SakhujaIcahn School of Medicine at Mount Sinai.
Benjamin S GlicksbergIcahn School of Medicine at Mount Sinai.
Girish NadkarniThe Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Medical Center, NY, USA.ORCID 0000-0001-6319-4314
Eyal KlangMount Sinai.ORCID 0000-0002-4567-3108

Funding

Conduits: Mount Sinai Health System Translational Science HubUL1TR004419 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rosalind J Wright · 2022 to 2026
$46.4M
COVID and Translational Science supercomputer (CATS)S10OD030463 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2021 to 2021
$2.0M
Big Omics Data Engine 2 SupercomputerS10OD026880 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2019 to 2019
$2.0M
NCATS NIH HHS UL1 TR004419NIH HHS S10 OD026880NIH HHS S10 OD030463
6 · The paper itself

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

PMID41890849
PMCPMC13015605

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.