Article

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

2026-02-24

Abstract excerpt

<h4>Background</h4> Diagnostic errors are a leading cause of preventable patient harm, often occurring during early clinical encounters where diagnostic uncertainty is maximal. Large language models (LLMs) have shown potential in medical reasoning, yet their ability to function as a diagnostic safety net, specifically by identifying and correcting human diagnostic errors, remains systematically unquantified. We e...

Topics

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

Identifiers and source

Literature Corpus work: 11cdd720-dfa1-5de1-a780-8f0ee01c3577
DOI: 10.64898/2026.02.22.26346832

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Evaluating the AI Potential as a Safety Net for Diagnosis: A Novel Benchmark of Large Language Models in Correcting Diagnostic ErrorsDOI 10.64898/2026.02.22.26346832Read

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

Evaluating Diagnostic Accuracy and Clinical Reasoning of Multiple Large Language Models in PsychiatrySemantic proximity Read

Evaluating Large Language Model Diagnostic Performance on JAMA Clinical Challenges via a Multi-Agent Conversational FrameworkSemantic 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

One is Not Enough: Multi-Agent Conversation Framework Enhances Rare Disease Diagnostic Capabilities of Large Language ModelsSemantic proximity Read

ChatGPT vs DeepSeek: A Comparative Study of Diagnostic Accuracy and Clinical Reasoning in Rare and Complex DiseasesSemantic proximity Read

Evaluating the Performance of Artificial Intelligence in Generating Differential Diagnoses for Infectious Diseases Cases: A Comparative Study of Large Language ModelsSemantic proximity Read

Development and retrospective validation of SCOUT: scalable clinical oversight of large language models via uncertainty triangulationSemantic proximity Read

Medical Hallucination in Foundation Models and Their Impact on HealthcareSemantic proximity Read

Emergent Misinformation Genesis in Multi-Agent LLM Clinical PipelinesSemantic proximity Read

Neurosymbolic Multi-Agent Artificial Intelligence versus General-Purpose Large Language Models for Clinical Decision Support in Ileus and VolvulusSemantic proximity Read

Dissecting clinical reasoning failures in frontier artificial intelligence using 10,000 synthetic casesSemantic proximity Read