TY - GEN
T1 - Completing A Systematic Review in Hours instead of Months with Interactive AI Agents
AU - Qiu, Rui
AU - Chen, Shijie
AU - Su, Yu
AU - Yen, Po Yin
AU - Shen, Han Wei
N1 - Publisher Copyright:
© 2025 Association for Computational Linguistics.
PY - 2025
Y1 - 2025
N2 - Systematic reviews (SRs) are vital for evidence-based practice in high stakes disciplines, such as healthcare, but are often impeded by labor-intensive 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. 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. InsightAgent demonstrates great potential in facilitating more timely and informed decision-making in high stake application scenarios.
AB - Systematic reviews (SRs) are vital for evidence-based practice in high stakes disciplines, such as healthcare, but are often impeded by labor-intensive 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. 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. InsightAgent demonstrates great potential in facilitating more timely and informed decision-making in high stake application scenarios.
UR - https://www.scopus.com/pages/publications/105021046444
U2 - 10.18653/v1/2025.acl-long.1523
DO - 10.18653/v1/2025.acl-long.1523
M3 - Conference contribution
AN - SCOPUS:105021046444
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 31559
EP - 31593
BT - Long Papers
A2 - Che, Wanxiang
A2 - Nabende, Joyce
A2 - Shutova, Ekaterina
A2 - Pilehvar, Mohammad Taher
PB - Association for Computational Linguistics (ACL)
T2 - 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025
Y2 - 27 July 2025 through 1 August 2025
ER -