Awesome Systematic Reviews: A Community-Driven Resource for Evidence Synthesis
Paweł Jemioło
Paper Short Abstract
Awesome Systematic Reviews is a collaborative GitHub repository curating resources on evidence synthesis, focusing on automation tools. It compiles tool features, documentation, and use cases, supporting researchers by centralizing key materials and encouraging community contributions.
Paper Abstract
Systematic reviews are fundamental to evidence-based decision-making, particularly in healthcare, public policy, and other research-driven fields. However, their rigorous methodology makes them highly time-consuming and resource-intensive. Recent advancements in Artificial Intelligence (AI) and automation are transforming this process, accelerating tasks such as literature screening, data extraction, and synthesis while maintaining methodological rigor. Awesome Systematic Reviews is an open, community-driven GitHub repository (https://github.com/pawljmlo/awesome-systematic-reviews) designed to centralize and curate resources related to evidence synthesis, with a focus on automation tools, including AI-powered solutions.
This initiative systematically catalogs tools that support various stages of systematic reviews and guideline development, including searching, study screening, data extraction, and risk-of-bias assessment. To ensure researchers have access to up-to-date information, we are conducting a scoping review of existing reviews that describe these tools. The repository compiles essential details such as tool functionalities, links to documentation, relevant research papers, pricing models (open-source vs. paid), and the specific phases of evidence synthesis in which each tool can be applied. Beyond automation tools, the repository also serves as a hub for guidelines, events, and research networks related to evidence synthesis.
A defining feature of Awesome Systematic Reviews is its collaborative, open-access nature. Anyone can contribute by submitting issues or pull requests to suggest new tools, provide feedback, or share experiences. As AI and automation continue to evolve, our goal is to maintain a dynamic, community-driven resource that grows alongside the field. We invite researchers, developers, and methodologists to engage with Awesome Systematic Reviews, helping to build a shared knowledge hub.
Accepted Poster
Paper Short Abstract
Paper Abstract
Systematic reviews are fundamental to evidence-based decision-making, particularly in healthcare, public policy, and other research-driven fields. However, their rigorous methodology makes them highly time-consuming and resource-intensive. Recent advancements in Artificial Intelligence (AI) and automation are transforming this process, accelerating tasks such as literature screening, data extraction, and synthesis while maintaining methodological rigor. Awesome Systematic Reviews is an open, community-driven GitHub repository (https://github.com/pawljmlo/awesome-systematic-reviews) designed to centralize and curate resources related to evidence synthesis, with a focus on automation tools, including AI-powered solutions.
This initiative systematically catalogs tools that support various stages of systematic reviews and guideline development, including searching, study screening, data extraction, and risk-of-bias assessment. To ensure researchers have access to up-to-date information, we are conducting a scoping review of existing reviews that describe these tools. The repository compiles essential details such as tool functionalities, links to documentation, relevant research papers, pricing models (open-source vs. paid), and the specific phases of evidence synthesis in which each tool can be applied. Beyond automation tools, the repository also serves as a hub for guidelines, events, and research networks related to evidence synthesis.
A defining feature of Awesome Systematic Reviews is its collaborative, open-access nature. Anyone can contribute by submitting issues or pull requests to suggest new tools, provide feedback, or share experiences. As AI and automation continue to evolve, our goal is to maintain a dynamic, community-driven resource that grows alongside the field. We invite researchers, developers, and methodologists to engage with Awesome Systematic Reviews, helping to build a shared knowledge hub.
Poster session
Session 1 Tuesday 1 July, 2025, -