ArticleArtificial intelligence review2026
An auditable pipeline for fuzzy full-text screening in systematic reviews: integrating contrastive semantic highlighting and LLM judgment.
Article in Artificial intelligence review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Full-text screening is the major bottleneck of systematic reviews (SRs), particularly in domains such as population health modelling of noncommunicable diseases (NCDs), where decisive eligibility information is scattered across long and heterogeneous full texts. In this methodological study, we introduce a scalable and auditable pipeline that reframes inclusion and exclusion as a fuzzy decision process. We evaluate the approach within the Population Health Modelling Consensus Reporting Network (POPCORN) and benchmark it against statistical and crisp baselines. Articles are parsed into overlapping chunks and embedded with a domain-adapted model; for each criterion (Population, Intervention, Outcome, Study Approach), we compute contrastive similarity (inclusion-exclusion cosine) and a
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