ArticleCureus2026
From Living Systematic Reviews to Fully AI-Driven Reviews.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) holds great promise for improving systematic reviews, particularly as the number of reviews keeps growing. A solution to keep systematic reviews up to date are the so-called living systematic reviews. Although they could be a solution, they currently face challenges regarding everything having to be done manually. A potential solution could be AI-driven reviews that automate each step, from literature searching and translation to data extraction and analysis. Potentially, AI could search through large amounts of studies, sort them by relevance, and extract relevant data. This significantly reduces the workload for researchers, and thereby gives researchers time to focus on oversight rather than manual tasks. However, these methods raise concerns about algorithmic bias and transparency. Proper training, clear ethical guidelines, and interdisciplinary collaboration are keys to ensuring quality and integrity. AI-driven reviews may become essential for efficiently handling the expanding scientific literature.
Indexed as
Identifiers
What OpenQuestion holds
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.