ArticleEnvironmental evidence2025
AI-assisted evidence screening method for systematic reviews in environmental research: integrating ChatGPT with domain knowledge.
Article in Environmental evidence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Existing evidence on the impact of climate risk on real estate valuations: a systematic map.Environmental evidence · 2026Article
- Assessing the effectiveness of ontology-grounded AI term extraction using OntoGPT for environmental evidence synthesis.Environmental evidence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systematic reviews (SRs) in environmental science is challenging due to diverse methodologies, terminologies, and study designs across disciplines. A major limitation is that inconsistent application of eligibility criteria in evidence-screening affects the reproducibility and transparency of SRs. To explore the potential role of Artificial Intelligence (AI) in applying eligibility criteria, we developed and evaluated an AI-assisted evidence-screening framework using a case study SR on the relationship between stream fecal coliform concentrations and land use and land cover (LULC). The SR incorporates publications from hydrology, ecology, public health, landscape, and urban planning, reflecting the interdisciplinary nature of environmental research. We fine-tuned ChatGPT-3.5 Turbo model with expert-reviewed training data for title, abstract, and full-text screening of 120 articles. The AI model demonstrated substantial agreement at title/abstract review and moderate agreement at full-text review with expert reviewers and maintained internal consistency, suggesting its potential for structured screening assistance. The findings provide a structured framework for applying eligibility criteria consistently, improving evidence screening efficiency, reducing labor and costs, and informing large language models (LLMs) integration in environmental SRs. Combining AI with domain knowledge provides an exploratory step to evaluate feasibility of AI-assisted evidence screening, especially for diverse, large volume, and interdisciplinary studies. Additionally, AI-assisted screening has the potential to provide a structured approach for managing disagreement among researchers with diverse domain knowledge, though further validation is needed.
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.