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    Using AI in evidence synthesis safely

    28 min
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    |
    6. Apr. 2026
    AIScienceTechnology

    Manual research reviews often miss key data. Learn how the new RAISE framework ensures AI transparency while keeping human researchers accountable.

    Using AI in evidence synthesis safely

    Bestes Zitat aus Using AI in evidence synthesis safely

    “

    The technology might be disruptive, but our standards are non-negotiable. You can use AI, but the human researcher stays 100% accountable for every single finding.

    ”

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    Eingabefrage

    Position Statement on Artificial Intelligence (AI) Use in Evidence Synthesis Across Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence 2025 https://onlinelibrary.wiley.com/doi/10.1002/cl2.70074

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    Kernaussagen

    1

    The AI Guardrails for Research

    0:00

    Nia: I was just thinking about how much pressure researchers are under to stay current, especially when you realize that in rapid reviews, a single person screening abstracts has about a 13% risk of accidentally missing a relevant study. It’s a huge margin for error!

    0:15

    Eli: It really is, and that’s exactly why the big players—Cochrane, Campbell, JBI, and the CEE—just released a unified position statement on using AI to help close those gaps. The headline is clear: you can use AI, but the human researcher stays 100% accountable for every single finding.

    0:34

    Nia: Right, so it’s not about handing the keys over to a "black box" algorithm. It’s about using these tools without compromising rigor or integrity.

    0:43

    Eli: Exactly. They’re backing a framework called RAISE to ensure that if a machine makes a judgment, it’s fully transparent. Let’s explore how these new requirements for disclosure and human oversight actually work in practice.

    2

    Defining the Accountability Frontier

    3

    Navigating the Transparency Mandate

    4

    The Developer's New Responsibility

    5

    Balancing Trade-offs in the Research Process

    6

    Human Oversight as the Ultimate Filter

    7

    The Global Infrastructure of Evidence

    8

    A Practical Playbook for the Researcher

    9

    Closing Reflection and Moving Forward

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