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Related Experiment Video

Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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Assessing the methodologic quality of systematic reviews using generative large language models.

Bowen Yao1,2, Onuralp Ergun1,2, Maylynn Ding2

  • 1Minneapolis VA Healthcare System, Minneapolis, MN, United States.

Canadian Urological Association Journal = Journal De L'Association Des Urologues Du Canada
|September 2, 2025
PubMed
Summary

Generative large language models (LLMs) show potential for assessing systematic review (SR) quality. With specific instructions, GPT achieved 93% accuracy in quality assessment, indicating efficient and reliable evaluation capabilities.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Informatics
  • Urology Research

Background:

  • Assessing the methodological quality of systematic reviews (SRs) is crucial for evidence-based medicine.
  • Generative large language models (LLMs) offer potential for automating complex analytical tasks.

Purpose of the Study:

  • To evaluate the accuracy of generative LLMs in assessing the methodological quality of urological SRs.
  • To compare LLM-based quality assessment with human expert penilaian.

Main Methods:

  • 114 urological SRs were assessed by human experts and a customized GPT model.
  • GPT underwent three zero-shot assessment iterations and an enhanced trial using chain-of-thought prompting.
  • Performance metrics included accuracy, sensitivity, specificity, and F1 score against human judgments.

Main Results:

  • GPT achieved 75% overall congruence with human reviewers, with 77% for critical criteria.
  • The average F1 score was 0.66, and internal validity was high at 85%.
  • Enhanced prompting improved critical criteria congruence to 91% and overall accuracy to 93%.

Conclusions:

  • Generative LLMs demonstrate a promising capacity for efficient and accurate quality assessment of SRs in urology.
  • LLM-based tools can potentially streamline the review process and support evidence synthesis.