mlsanityData to 7 Oct 2026

Completing A Systematic Review in Hours instead of Months with Interactive AI Agents

arXiv 2504.14822 ↗First posted 21 Apr 2025cs.HC cs.CLcited by 4

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

  1. 2024HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents5 Mar
  2. 2024ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary5 Mar

Other references · 7

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2024LitLLM: A Toolkit for Scientific Literature Reviewcited by 61
  3. 2024Instruct Large Language Models to Generate Scientific Literature Survey Step by Stepcited by 10
  4. 2024pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomycited by 6
  5. 2023From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Makingcited by 4
  6. 2014Toward a deeper understanding of Visualization through keyword analysiscited by 3
  7. 2025VADIS: A Visual Analytics Pipeline for Dynamic Document Representation and Information-Seekingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024System for systematic literature review using multiple AI agents: Concept and an empirical evaluation
  2. 2025Can Agents Judge Systematic Reviews Like Humans? Evaluating SLRs with LLM-based Multi-Agent System
  3. 2025LatteReview: A Multi-Agent Framework for Systematic Review Automation Using Large Language Models
  4. 2026Checkpoints Are Not Enough: Trust Calibration in CoSLR, a Human-AI System for Systematic Literature Reviews
  5. 2025LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation
  6. 2025FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights
  7. 2026meta-pipe: An LLM-agent pipeline for end-to-end automated systematic review and meta-analysis
  8. 2026Evaluating AI-based Scientific Knowledge Synthesis with Epidemiological Systematic Reviews