Navigating Complexity: Enhancing Pediatric Diagnostics With Large Language Models*

Author:

Mitchell James1,Bennett Tellen D.12

Affiliation:

1. Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO.

2. Department of Pediatrics (Critical Care Medicine), University of Colorado School of Medicine, Aurora, CO.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Reference10 articles.

1. Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation.;Wen;npj Digit Med,2019

2. Machine learning approach to predicting absence of serious bacterial infection at PICU admission.;Martin;Hosp Pediatr,2022

3. Continuous prediction of mortality in the PICU: A recurrent neural network model in a single-center dataset.;Aczon;Pediatr Crit Care Med,2021

4. Revolutionizing healthcare: The role of artificial intelligence in clinical practice.;Alowais;BMC Med Educ,2023

5. Comparing the Quality of Domain-Specific Versus General Language Models for Artificial Intelligence-Generated Differential Diagnoses in PICU Patients.;Akhondi-Asl;Pediatr Crit Care Med,2024

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