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Volume 14
Issue 17
10.3390/jcm14176011
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Review

Pulmonary and Immune Dysfunction in Pediatric Long COVID: A Case Study Evaluating the Utility of ChatGPT-4 for Analyzing Scientific Articles

by
Susanna R. Var
1,2,*,
Nicole Maeser
3,
Jeffrey Blake
4,
Elise Zahs
4,
Nathan Deep
4,
Zoey Vasilakos
4,
Jennifer McKay
5,
Sether Johnson
1,2,
Phoebe Strell
1,2,6,
Allison Chang
7,
Holly Korthas
8,
Venkatramana Krishna
9,
Manojkumar Narayanan
9,
Tuhinur Arju
9,
Dilmareth E. Natera-Rodriguez
1,2,
Alex Roman
7,
Sam J. Schulz
5,
Anala Shetty
1,2,
Mayuresh Vernekar
4,
Madison A. Waldron
7,
Kennedy Person
7,
Maxim Cheeran
9,
Ling Li
7,8,10 and
Walter C. Low
1,2,3,4,6,7,10,11,*add Show full author list remove Hide full author list
1
Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, USA
2
Stem Cell Institute, University of Minnesota, Minneapolis, MN 55455, USA
3
Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, MN 55455, USA
4
College of Biological Sciences, University of Minnesota, Minneapolis, MN 55455, USA
5
Medical School, University of Minnesota, Minneapolis, MN 55455, USA
6
Comparative and Molecular Biosciences Graduate Program, University of Minnesota, Minneapolis, MN 55455, USA
7
Neuroscience Graduate Program, University of Minnesota, Minneapolis, MN 55455, USA
8
Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
9
Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN 55455, USA
10
Molecular Pharmacology and Therapeutics Graduate Program, University of Minnesota, Minneapolis, MN 55455, USA
11
Molecular, Cellular, Developmental Biology, and Genetics Graduate Program, University of Minnesota, Minneapolis, MN 55455, USA
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Authors to whom correspondence should be addressed.
J. Clin. Med. 2025, 14(17), 6011; https://doi.org/10.3390/jcm14176011 (registering DOI)
Submission received: 21 May 2025 / Revised: 16 August 2025 / Accepted: 20 August 2025 / Published: 25 August 2025
(This article belongs to the Section Epidemiology & Public Health)
Versions Notes

Abstract

Coronavirus disease 2019 (COVID-19) in adults is well characterized and associated with multisystem dysfunction. A subset of patients develop post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID), marked by persistent and fluctuating organ system abnormalities. In children, distinct clinical and pathophysiological features of COVID-19 and long COVID are increasingly recognized, though knowledge remains limited relative to adults. The exponential expansion of the COVID-19 literature has made comprehensive appraisal by individual researchers increasingly unfeasible, highlighting the need for new approaches to evidence synthesis. Large language models (LLMs) such as the Generative Pre-trained Transformer (GPT) can process vast amounts of text, offering potential utility in this domain. Earlier versions of GPT, however, have been prone to generating fabricated references or misrepresentations of primary data. To evaluate the potential of more advanced models, we systematically applied GPT-4 to summarize studies on pediatric long COVID published between January 2022 and January 2025. Articles were identified in PubMed, and full-text PDFs were retrieved from publishers. GPT-4-generated summaries were cross-checked against the results sections of the original reports to ensure accuracy before incorporation into a structured review framework. This methodology demonstrates how LLMs may augment traditional literature review by improving efficiency and coverage in rapidly evolving fields, provided that outputs are subjected to rigorous human verification.
Keywords: pulmonary dysfunction; immune dysfunction; pediatric population; coronavirus; long COVID; post-acute sequelae of COVID-19; artificial intelligence; ChatGPT; large language model; SAR-CoV-2 pulmonary dysfunction; immune dysfunction; pediatric population; coronavirus; long COVID; post-acute sequelae of COVID-19; artificial intelligence; ChatGPT; large language model; SAR-CoV-2

Share and Cite

MDPI and ACS Style

Var, S.R.; Maeser, N.; Blake, J.; Zahs, E.; Deep, N.; Vasilakos, Z.; McKay, J.; Johnson, S.; Strell, P.; Chang, A.; et al. Pulmonary and Immune Dysfunction in Pediatric Long COVID: A Case Study Evaluating the Utility of ChatGPT-4 for Analyzing Scientific Articles. J. Clin. Med. 2025, 14, 6011. https://doi.org/10.3390/jcm14176011

AMA Style

Var SR, Maeser N, Blake J, Zahs E, Deep N, Vasilakos Z, McKay J, Johnson S, Strell P, Chang A, et al. Pulmonary and Immune Dysfunction in Pediatric Long COVID: A Case Study Evaluating the Utility of ChatGPT-4 for Analyzing Scientific Articles. Journal of Clinical Medicine. 2025; 14(17):6011. https://doi.org/10.3390/jcm14176011

Chicago/Turabian Style

Var, Susanna R., Nicole Maeser, Jeffrey Blake, Elise Zahs, Nathan Deep, Zoey Vasilakos, Jennifer McKay, Sether Johnson, Phoebe Strell, Allison Chang, and et al. 2025. "Pulmonary and Immune Dysfunction in Pediatric Long COVID: A Case Study Evaluating the Utility of ChatGPT-4 for Analyzing Scientific Articles" Journal of Clinical Medicine 14, no. 17: 6011. https://doi.org/10.3390/jcm14176011

APA Style

Var, S. R., Maeser, N., Blake, J., Zahs, E., Deep, N., Vasilakos, Z., McKay, J., Johnson, S., Strell, P., Chang, A., Korthas, H., Krishna, V., Narayanan, M., Arju, T., Natera-Rodriguez, D. E., Roman, A., Schulz, S. J., Shetty, A., Vernekar, M., ... Low, W. C. (2025). Pulmonary and Immune Dysfunction in Pediatric Long COVID: A Case Study Evaluating the Utility of ChatGPT-4 for Analyzing Scientific Articles. Journal of Clinical Medicine, 14(17), 6011. https://doi.org/10.3390/jcm14176011

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