ArticleEuropean urology open science2025
Using Artificial Intelligence for Text Screening in a Systematic Review of Cardiotoxicity.
Article in European urology open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and objective: Artificial intelligence (AI), capable of analyzing vast volume of data rapidly, presents a promising solution to optimize literature screening for systematic reviews (SRs). Using the INSIDE (artificial INtelligence to Support Informed DEcision making) platform, we compared the performance of AI against the "gold standard" traditional SR method in the context of prostate cancer (PC) to assess whether AI could potentially improve efficiency and quality of screening. Methods: Publications from traditional screening of four SRs (focused on PC therapies and potential cardiotoxicity) were compared with the AI-based approach. Publications were ranked based on relevance scores. Work saved over sampling (WSS), that is, efforts saved by automatically excluding nonrelevant publications, determined efficiency. For a quality analysis, data visualization using a scatter plot suggested the proportions of "relevant," "irrelevant," and "not screened" records. Key findings and limitations: For AI-based screening, an efficiency analysis used publications from the traditional approach ( Conclusions: This study confirms the impact of an AI-based approach in optimizing the SR process. It highlights best practices and benchmarks to assess the efficiency and possibly quality of literature screening, supporting the integration of AI into future SRs. Patient summary: Systematic reviews (SRs) help create a detailed and unbiased summary on a specific research question. This summary is based on published information. Development of SRs using the traditional method requires detailed in-person review of the records, which takes a lot of time and effort. With the use of artificial intelligence (AI), the key data from a large amount of text are identified faster. This process requires a review of fewer records to find the most relevant ones, which saves time. The aim of this study was to understand how an AI tool, known as INSIDE PC, could help with SRs. This study looked at how well INSIDE PC worked compared with the traditional method for SRs. The AI method scored articles based on their relevance with respect to the topic of this SR. Data visuals or graphs were used to compare data points and remove irrelevant records from the review. This process decreased workload and saved time. The AI method also used a learning algorithm known as active learning. This helps AI tools learn from a small training sample data. Useful records were identified much faster by this method, with less efforts. The results showed that AI could improve the ease and speed of reviewing records for SRs. It is important that these AI methods are tested and improved to meet the needs of SRs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.