Article

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

2026-04-10

Abstract excerpt

<title>Abstract</title> <p> <bold>Background</bold> General-purpose large language models (LLMs) demonstrate variable diagnostic accuracy and residual hallucination when applied to complex surgical emergencies. Whether a neurosymbolic multi-agent architecture—integrating domain-specific vision-language models, medically fine-tuned reasoning engines, and compositional verification agents—can outperform monolithi...

Topics

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

Identifiers and source

Literature Corpus work: 46301a49-07e2-5a0d-b39c-cb4946c0e434
DOI: 10.21203/rs.3.rs-9045948/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Neurosymbolic Multi-Agent Artificial Intelligence versus General-Purpose Large Language Models for Clinical Decision Support in Ileus and VolvulusDOI 10.21203/rs.3.rs-9045948/v1Read

Artificial Intelligence - Large Language Models (LLMs) and Agentic AI in Clinical Decision Support in Outpatient, General Practice and Acute Settings: A Systematic Review of Diagnostic Accuracy, Comparative Efficacy, Clinical Outcomes and Implementation FactorsSemantic 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

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

A Systematic Review of Large Language Models in Medical Specialties: Applications, Challenges and Future DirectionsSemantic proximity Read

Performance Comparison of a Neuro-Symbolic Large Language Model System Versus Conventional AI Models and Human Experts in Cholangitis ManagementSemantic proximity Read

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

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

AI Agents in Clinical Medicine: A Systematic ReviewSemantic proximity Read

Diagnostic Accuracy of Large Language Models for Rare Diseases: A Systematic Review and Meta-AnalysisSemantic proximity Read

AI vs Human Performance in Conversational Hospital-Based Neurological DiagnosisSemantic proximity Read

MedAgent-Pro: Towards Evidence-Based Multi-Modal Medical Diagnosis via Reasoning Agentic WorkflowSemantic proximity Read