Article

Drug or Pokémon? Large language model performance in identification of fabricated medications

2026-01-13

Abstract excerpt

<h4>Background</h4> Large language models (LLMs) are increasingly used in medication-related tasks despite limited evidence supporting their accuracy and safety. LLMs are vulnerable to adversarial attacks and may confabulate in response to fabricated information in the prompt. Given the substantial risk of harm from medication errors, the purpose of this study was to establish a benchmark task to evaluate LLM per...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 3515e02a-f198-5e43-a1fb-12f657b7bf3f
DOI: 10.64898/2026.01.12.26343930

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Drug or Pokémon? Large language model performance in identification of fabricated medicationsDOI 10.64898/2026.01.12.26343930Read

Large Language Models Are Highly Vulnerable to Adversarial Hallucination Attacks in Clinical Decision Support: A Multi-Model Assurance AnalysisSemantic proximity Read

A Three-Tier Operational Benchmark for Evaluating Large Language Models on Hospital Medication SafetySemantic proximity Read

Rethinking Medical LLM Hallucinations: A System-Level SurveySemantic proximity Read

Rx-LLM: a benchmarking suite to evaluate safe large language model performance for medication-related tasksSemantic proximity Read

Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language ModelsSemantic proximity Read

Learning the Phenotype of Medical HallucinationsSemantic proximity Read

Red-Teaming Medical AI: Systematic Adversarial Evaluation of LLM Safety Guardrails in Clinical ContextsSemantic proximity Read

An interdisciplinary, randomized, single-blind evaluation of state-of-the-art large language models for their implications and risks in medical diagnosis and managementSemantic proximity Read

Leveraging simulation to provide a practical framework for assessing the novel scope of risk of LLMs in healthcareSemantic proximity Read

Evaluating Large Language Models’ Performance in FDA Regulatory ScienceSemantic proximity Read

Generative AI and Large Language Models in Reducing Medication Related Harm and Adverse Drug Events – A Scoping ReviewSemantic proximity Read

Reliability of large language model knowledge across brand and generic cancer drug namesSemantic proximity Read