{"id":114559,"date":"2026-09-11T15:08:25","date_gmt":"2026-09-11T13:08:25","guid":{"rendered":"https:\/\/nilu.gnist.dev\/publikasjoner\/an-introduction-to-prismaid-open-source-and-open-science-ai-for-advancing-information-extraction-in-systematic-reviews\/"},"modified":"2026-09-11T15:08:25","modified_gmt":"2026-09-11T13:08:25","slug":"an-introduction-to-prismaid-open-source-and-open-science-ai-for-advancing-information-extraction-in-systematic-reviews","status":"publish","type":"nva_publication","link":"https:\/\/nilu.gnist.dev\/en\/publications\/an-introduction-to-prismaid-open-source-and-open-science-ai-for-advancing-information-extraction-in-systematic-reviews\/","title":{"rendered":"An Introduction to prismAId: Open-Source and Open Science AI for Advancing Information Extraction in Systematic Reviews"},"content":{"rendered":"<p class=\"wp-block-paragraph\">prismAId is an open-source tool designed to streamline systematic literature reviews by leveraging generative AI models for information extraction. It offers an accessible, efficient, and replicable method for extracting and analyzing data from scientific literature, eliminating the need for coding expertise. Supporting various review protocols, including PRISMA 2020, prismAId is distributed across multiple platforms \u2013 Go, Python, Julia, R \u2013 and provides user-friendly binaries compatible with Windows, macOS, and Linux. The tool integrates with leading large language models (LLMs) such as OpenAI\u2019s GPT series, Google\u2019s Gemini, Cohere\u2019s Command, and Anthropic\u2019s Claude, ensuring comprehensive and up-to-date literature analysis. prismAId facilitates systematic reviews, enabling researchers to conduct thorough, fast, and reproducible analyses, thereby advancing open science initiatives.<\/p>\n","protected":false},"template":"","meta":{"_acf_changed":false,"_searchwp_excluded":"","_id":"","_status":"PUBLISHED","_sync_date":"2026-09-11 13:55:02","footnotes":""},"nva_tax_category":[1099],"class_list":["post-114559","nva_publication","type-nva_publication","status-publish","hentry","nva_tax_category-scientific-journal-publication"],"acf":[],"_links":{"self":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/114559","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication"}],"about":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/types\/nva_publication"}],"version-history":[{"count":0,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/114559\/revisions"}],"wp:attachment":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/media?parent=114559"}],"wp:term":[{"taxonomy":"nva_tax_category","embeddable":true,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_tax_category?post=114559"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}