Article

LLM Reasoning Does Not Protect Against Clinical Cognitive Biases - An Evaluation Using BiasMedQA

2025-06-23

Abstract excerpt

<h4>Background</h4> Cognitive biases are an important source of clinical errors. Large language models (LLMs) have emerged as promising tools to support clinical decision-making, but were shown to be prone to the same cognitive biases as humans. Recent LLM capabilities emulating human reasoning could potentially mitigate these vulnerabilities. <h4>Methods</h4> To evaluate the impact of reasoning on susceptibilit...

Topics

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

Identifiers and source

Literature Corpus work: a0fb9488-e5b7-50da-b06f-597f27a1825e
DOI: 10.1101/2025.06.22.25330078

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

LLM Reasoning Does Not Protect Against Clinical Cognitive Biases - An Evaluation Using BiasMedQADOI 10.1101/2025.06.22.25330078Read

Understanding the Inner Workings of Large Language Models in MedicineSemantic proximity Read

Prompting is All You Need: How to Make LLMs More Helpful for Clinical Decision SupportSemantic proximity Read

Understanding the Inner Workings of Large Language Models in MedicineSemantic proximity Read

The Inefficacy of Artificial Intelligence Large Language Models in Healthcare: A Clinical and Statistical PerspectiveSemantic proximity Read

CognAlign: A Multi-Agent Cognitive-Alignment Framework for Transparent, Bias-Aware Medical Triage Using Small Language ModelsSemantic proximity Read

Evaluating the AI Potential as a Safety Net for Diagnosis: A Novel Benchmark of Large Language Models in Correcting Diagnostic ErrorsSemantic proximity Read

LLMs Can Do Medical Harm: Stress-Testing Clinical Decisions Under Social PressureSemantic 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

High Consistency, Limited Accuracy: Evaluating Large Language Models for Binary Medical DiagnosisSemantic proximity Read

The Inefficacy of Artificial Intelligence Large Language Models in Healthcare: A Clinical and Statistical PerspectiveSemantic proximity Read

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

Understanding the Inner Workings of Large Language Models in MedicineSemantic proximity Read