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

Systematic reviews (SRs) are vital for evidencebased practice in high stakes disciplines, such as healthcare, but are often impeded by laborintensive and lengthy processes that can span months.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 revolutionizes the systematic review workflow.In-sightAgent 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.InsightAgent demonstrates great potential in facilitating more timely and informed decisionmaking in high stake application scenarios 1 .

Authors

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.acl-long.1523
Citations
1
Primary Topic
Multi-Agent Systems and Negotiation
Type
article
Field-Weighted Citation Impact
2.48
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article

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

Po‐Yin Yen, Rui Qiu, Yu Su, Shijie Chen et al.
1 citations
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article

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

Po‐Yin Yen, Rui Qiu, Yu Su, Shijie Chen, Han Shen
article en
1 citations

Abstract

Systematic reviews (SRs) are vital for evidencebased practice in high stakes disciplines, such as healthcare, but are often impeded by laborintensive and lengthy processes that can span months.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 revolutionizes the systematic review workflow.In-sightAgent 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.InsightAgent demonstrates great potential in facilitating more timely and informed decisionmaking in high stake application scenarios 1 .

Openalex Percentile: Top 9%
Multi-Agent Systems and Negotiation
2.48
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