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Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support
Research article (Communications Medicine, 2025) · cited 74× · AI/ML
Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support
Summary
Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support is a scholarly article[1].
Key Facts
Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support. Retrieved May 24, 2026, from https://4ort.xyz/entity/multi-model-assurance-analysis-showing-large-language-models-are-highly-vulnerable-to-adversarial-hallucination-attacks-
MLA“Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/multi-model-assurance-analysis-showing-large-language-models-are-highly-vulnerable-to-adversarial-hallucination-attacks-.
BibTeX@misc{4ortxyz_multi-model-assurance-analysis-showing-large-language-models-are-highly-vulnerable-to-adversarial-hallucination-attacks-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support}}, year = {2026}, url = {https://4ort.xyz/entity/multi-model-assurance-analysis-showing-large-language-models-are-highly-vulnerable-to-adversarial-hallucination-attacks-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support — https://4ort.xyz/entity/multi-model-assurance-analysis-showing-large-language-models-are-highly-vulnerable-to-adversarial-hallucination-attacks- (retrieved 2026-05-24)
