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Artificial Intelligence Applications Versus Manual Methods For Literature Retrieval: A Comparative Analysis.

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Artificial Intelligence Applications Versus Manual Methods For Literature Retrieval: A Comparative Analysis.

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Related Experiment Video

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Artificial Intelligence Applications Versus Manual Methods For Literature Retrieval: A Comparative Analysis.

Jenny O'Rourke¹, Matthew Byrne², Ginger Schroers³

¹Parkinson School of Health Sciences and Public Health, Loyola University Chicago, IL, USA.

Western Journal of Nursing Research

|June 10, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Generative artificial intelligence (AI) tools show potential for literature reviews but have limited accuracy in identifying references. AI applications may assist, but cannot replace human expertise in scholarly writing.

Keywords:

artificial intelligence large language models literature search nursing nursing research

Related Experiment Videos

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Area of Science:

Nursing Education
Scholarly Writing
Artificial Intelligence

Background:

Generative artificial intelligence (AI) and large language models are increasingly used in nursing education, practice, and scholarly writing.
AI applications show promise in reducing production time for scholarly work, particularly literature reviews.
However, the accuracy of AI in identifying references for literature reviews remains under-investigated.

Purpose of the Study:

To compare the accuracy of AI-generated citations with human-generated citations in literature reviews.
To evaluate the performance of AI literature review applications in reference identification.

Main Methods:

A comparative exploratory design was employed.
References from four human-written literature reviews (two published, two unpublished) were compared with references from two AI applications (Consensus and Elicit).
Three prompting strategies, including ChatGPT-4, were used to evaluate AI performance.

Main Results:

The agreement between AI and human-generated reference lists varied significantly, ranging from 0% to 63.6%.
The Consensus application demonstrated a higher mean match rate (21.3%) compared to Elicit (3.7%).
The use of ChatGPT-4 prompts and the publication status of reviews did not significantly affect the results.

Conclusions:

The examined AI literature review applications have potential but also limitations.
AI tools may support, but not substitute, human effort in scholarly literature review processes.