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Using Artificial Intelligence for Text Screening in a Systematic Review of Cardiotoxicity.

Steven E Canfield1, Moez Karim Aziz2, Muhammad Imran Omar3

  • 1University of Texas McGovern Medical School, Houston, TX, USA.

European Urology Open Science
|October 13, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) significantly optimizes systematic reviews (SRs) by improving literature screening efficiency. This AI-driven approach, demonstrated with prostate cancer data, reduces the workload and time required for comprehensive reviews.

Keywords:
Artificial intelligenceINSIDE PCLiterature screeningMachine learningProstate cancerSystematic literature review

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Systematic Review Methodology

Background:

  • Traditional systematic reviews (SRs) involve time-consuming manual literature screening.
  • Artificial intelligence (AI) offers potential for rapid data analysis and optimization of SR processes.
  • The INSIDE platform was developed to support AI-driven decision-making in research.

Purpose of the Study:

  • To compare the efficiency and quality of AI-based literature screening against traditional methods for systematic reviews.
  • To assess the performance of the INSIDE platform in the context of prostate cancer research.
  • To determine if AI can enhance the speed and accuracy of identifying relevant publications.

Main Methods:

  • Comparative analysis of AI-based screening (INSIDE platform) versus traditional methods using four SRs on prostate cancer.
  • Evaluation of efficiency using Work Saved Over Sampling (WSS) metrics at 80% and 95% relevant publication identification.
  • Quality assessment through data visualization (scatter plots) to categorize records as relevant, irrelevant, or not screened.

Main Results:

  • AI-based screening demonstrated higher efficiency, requiring fewer publications to identify key relevant records (WSS@80% 20.3%, WSS@95% 9.4%).
  • Active learning within the AI approach further increased screening efficiency (WSS@80% 54.0%, WSS@95% 54.8%).
  • Data visualization aided in broader search result analysis and identification of outlier articles.

Conclusions:

  • AI-based approaches significantly optimize the systematic review process, enhancing efficiency and potentially quality.
  • The study provides benchmarks for assessing AI tool performance in literature screening.
  • Integration of AI into future systematic reviews is supported, with a need for continued testing and refinement.