Article

Large language models for automatable real-world performance monitoring of diagnostic decision support systems: a comparison to manual doctor panel review in a prospective clinical study

2025-11-20

Abstract excerpt

<title>Abstract</title> <p>Background Diagnostic decision support systems (DDSS) are increasingly deployed at scale, yet their diagnostic accuracy is insufficiently monitored once integrated into care. Traditional post-market surveillance relies on clinician review, which is costly, slow, and difficult to sustain. Large language models (LLMs) may offer a scalable and potentially automatable solution, but their pe...

Topics

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

Identifiers and source

Literature Corpus work: 5865d042-9747-53af-ac36-7fa3244e039f
DOI: 10.21203/rs.3.rs-8022874/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Large language models for automatable real-world performance monitoring of diagnostic decision support systems: a comparison to manual doctor panel review in a prospective clinical studyDOI 10.21203/rs.3.rs-8022874/v1Read

Large Language Models for Automated Icd-10 Coding of Obstetric Clinical Notes in Portuguese: Comparison With Human CodersSemantic proximity Read

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

Developing an open-source framework for LLM evaluation of patients using EHR clinical documentation; performance of LLMs relative to medical professionalsSemantic proximity Read

Assessing the Limitations of Large Language Models in Clinical Practice Guideline-concordant Treatment Decision-making on Real-world DataSemantic proximity Read

Automatic ICD coding using LLMs: a systematic reviewSemantic proximity Read

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

Systematic Literature Review of LLM-Large Language Model in Medical: Digital Health, Technology and ApplicationsSemantic proximity Read

Medical errors in large language models revealed using 1,000 synthetic clinical transcriptsSemantic proximity Read

Validation of 13,102 ICD-10-CM Codes Using a Large Language Model-Based SystemSemantic proximity Read

Performance evaluation and benchmarking across 16 large language models on a comprehensive real-world emergency department triage data setSemantic proximity Read

Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping AlgorithmsSemantic proximity Read