Completing A Systematic Review in Hours instead of Months with Interactive AI Agents
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
Systematic reviews (SRs) are vital for evidence-based practice in high stakes disciplines, such as healthcare, but are often impeded by intensive labors and lengthy processes that can take months to complete. Due to the high demand for domain expertise, existing automatic summarization methods fail to accurately identify relevant studies and generate high-quality summaries. To that end, we introduce InsightAgent, a human-centered interactive AI agent powered by large language models that revolutionize this workflow. InsightAgent partitions a large literature corpus based on semantics and employs a multi-agent design for more focused processing of literature, leading to significant improvement in the quality of generated SRs. InsightAgent also provides intuitive visualizations of the corpus and agent trajectories, allowing users to effortlessly monitor the actions of the agent and provide real-time feedback based on their expertise. Our user studies with 9 medical professionals demonstrate that the visualization and interaction mechanisms can effectively improve the quality of synthesized SRs by 27.2%, reaching 79.7% of human-written quality. At the same time, user satisfaction is improved by 34.4%. With InsightAgent, it only takes a clinician about 1.5 hours, rather than months, to complete a high-quality systematic review.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 51 references that are in the map. First posted on arXiv 21 Apr 2025.
Builds on
9 of 51 references.
Closest ancestors · 2
Same direction, newest first
- 2024HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents5 Mar
- 2024ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary5 Mar
Other references · 7
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2024LitLLM: A Toolkit for Scientific Literature Reviewcited by 61
- 2024Instruct Large Language Models to Generate Scientific Literature Survey Step by Stepcited by 10
- 2024pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomycited by 6
- 2023From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Makingcited by 4
- 2014Toward a deeper understanding of Visualization through keyword analysiscited by 3
- 2025VADIS: A Visual Analytics Pipeline for Dynamic Document Representation and Information-Seekingcited by 3
Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2026AutoForest: Automatically Generating Forest Plots from Biomedical Studies with End-to-End Evidence Extraction and Synthesiscited by 1
- 2025Meow: End-to-End Outline Writing for Automatic Academic Surveycited by 0
- 2026Probing the Future of Meta-Analysis: Eliciting Design Principles via an Agentic Research IDEcited by 0
- 2026SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentationcited by 0
- 2026SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation5 Sep 2026
- 2026AutoForest: Automatically Generating Forest Plots from Biomedical Studies with End-to-End Evidence Extraction and Synthesis1 Jun 2026
- 2026Probing the Future of Meta-Analysis: Eliciting Design Principles via an Agentic Research IDE26 Jan 2026
- 2025Meow: End-to-End Outline Writing for Automatic Academic Survey19 Sep 2025
Similar, not cited
Close in wording, no citation either way
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- 2025LatteReview: A Multi-Agent Framework for Systematic Review Automation Using Large Language Models
- 2026Checkpoints Are Not Enough: Trust Calibration in CoSLR, a Human-AI System for Systematic Literature Reviews
- 2025LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation
- 2025FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights
- 2026meta-pipe: An LLM-agent pipeline for end-to-end automated systematic review and meta-analysis
- 2026Evaluating AI-based Scientific Knowledge Synthesis with Epidemiological Systematic Reviews