A4SLR: An Agentic AI-Assisted Systematic Literature Review Framework to Augment Evidence Synthesis for HEOR and HTA.

Authors

Kyeryoung Lee, Hunki Paek, Nneka Ofoegbu, Steven Rube, Mitchell K Higashi, Dalia Dawoud, Hua Xu, Lizheng Shi, Xiaoyan Wang

Published in

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. Aug 13, 2025. Epub Aug 13, 2025.

Abstract

Systematic literature reviews (SLRs) are essential for synthesizing high-quality evidence in clinical research, health economics and outcome research (HEOR), and health technology assessments (HTAs). However, the growing volume of published data has made SLRs time-consuming, labor-intensive, and costly. To address these challenges, we introduce A4SLR, an Agentic Artificial intelligence (AI)-Assisted SLR framework, that provides a flexible, extensible methodology for automating the entire SLR process-from initial query formulation to evidence synthesis-across various study fields.
A4SLR comprises eight modules integrated with specialized AI agents powered by large language models: Search, I/E criteria deployment, Abstract/full-text screening, Text/table pre-processing, Data extraction, Assessment, Risk of bias analysis, and Report. We implemented and validated this framework using two use cases, non-small cell lung cancer and perinatal mood and anxiety disorders. Performance of the assessment was evaluated quantitatively and qualitatively.
Our implementation demonstrated high accuracy in article screening (F1 scores:0.917-0.977), risk of bias assessment (Cohen's κ:0.8442-0.9064), and data extraction (F-scores:0.96-0.998), including patient characteristics, safety and efficacy-outcomes, economic model parameters, and cost-effectiveness data. Notably, the Text/table pre-processing agent yielded comprehensive coverage of data elements, particularly in the challenging tasks of accurately matching outcome values to their corresponding study arms.
Our findings highlight the potential of the A4SLR framework to transform the evidence synthesis process by addressing the limitations of manual SLRs, thereby enhancing HEOR and HTAs. Designed as a scalable, user-centric, extensible approach, A4SLR provides a robust solution for generating comprehensive up-to-date evidence to support researchers and decision-makers across diverse clinical and therapeutic areas.

PMID:
40816692
Bibliographic data and abstract were imported from PubMed on 16 Aug 2025.

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