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

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.