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A Human-AI Collaborative Framework for Systematic Reviews
Li, Yingyin; Li, Jiao
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https://hdl.handle.net/2142/132982
Description
- Title
- A Human-AI Collaborative Framework for Systematic Reviews
- Author(s)
- Li, Yingyin
- Li, Jiao
- Issue Date
- 2026-03-12
- Keyword(s)
- Systematic review
- Human–AI collaboration
- Large language models
- Evidence-based medicine
- Abstract
- Systematic reviews (SRs) are a cornerstone of evidence-based medicine (EBM), yet traditional workflows are increasingly unsustainable due to the exponential growth of medical literature and the labor-intensive nature of manual appraisal. While Large Language Models (LLMs) offer significant automation potential, their integration into clinical synthesis is limited by reasoning hallucinations and a lack of procedural transparency in complex critical judgment tasks. This paper proposes a holistic, five-stage human–AI collaborative framework designed to support the entire SR lifecycle. The core innovation is the formalization of a tri-modal interaction engine comprising Evidence-Grounded, Logic-Augmented, and Consensus-Mediated modes, which dynamically allocates cognitive tasks between human experts and AI. Validated through a large-scale risk-of-bias case study involving 2,100 RCT samples across seven bias domains, the framework demonstrates that structured human-AI synergy can stabilize judgment consistency, mitigate logical errors, and balance resource allocation with scientific rigor.. This work provides a strategic blueprint for the transition of information professionals into designers and managers of intelligent research systems.
- Publisher
- iSchools
- Series/Report Name or Number
- iConference 2026 Proceedings
- Type of Resource
- Other
- Genre of Resource
- Conference Poster
- Language
- eng
- Permalink
- https://hdl.handle.net/2142/132982
- Copyright and License Information
- Copyright 2026 is held by Yingyin Li and Jiao Li. Copyright permissions, when appropriate, must be obtained directly from the authors.
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