Article

Synoptic Reporting by Summarizing Cancer Pathology Reports using Large Language Models

2024-04-29

Abstract excerpt

<h4>Background</h4> Synoptic reporting, the documenting of clinical information in a structured manner, is known to improve patient care by reducing errors, increasing readability, interoperability, and report completeness. Despite its advantages, manually synthesizing synoptic reports from narrative reports is expensive and error prone when the number of structured fields are many. While the recent revolutionary...

Topics

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

Identifiers and source

Literature Corpus work: 8c5673f3-8964-5f75-ba43-62ea2379a5c9
DOI: 10.1101/2024.04.26.24306452

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Synoptic Reporting by Summarizing Cancer Pathology Reports using Large Language ModelsDOI 10.1101/2024.04.26.24306452Read

Enhancing Doctor-Patient Communication Using Large Language Models for Pathology Report InterpretationSemantic proximity Read

The Transformative Potential of Large Language Models in Mining Electronic Health Records DataSemantic proximity Read

Large language models for cancer registry abstraction: a real-world evaluation across models, variables, and cancer typesSemantic proximity Read

Large language models for extracting histopathologic diagnoses of colorectal cancer and dysplasia from electronic health recordsSemantic proximity Read

ToKSA - Tokenized Key Sentence Annotation - a Novel Method for Rapid Approximation of Ground Truth for Natural Language ProcessingSemantic proximity Read

A Retrospective Evaluation of the Microsoft Healthcare Agent Orchestrator for Tumor Board Patient SummariesSemantic proximity Read

Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language modelsSemantic proximity Read

Large-Language Models for data extraction from written kidney biopsy reportsSemantic proximity Read

On the limitations of large language models in clinical diagnosisSemantic proximity Read

Generation and Evaluation of Realistic Synthetic Clinical Progress Notes for Prostate Cancer using Large Language ModelsSemantic proximity Read

Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in IBDSemantic proximity Read

Combining Clinician Expertise with Prompt Engineering enhances Small Language Models Reliability for Cancer Entity Recognition in Electronic Health RecordsSemantic proximity Read