Generative Artificial Intelligence for Systematic Literature Reviews: A Good Practices Report of an ISPOR Special Task Force.
TL;DR
Evidence supported GenAI use for high-recall title/abstract screening and structured first-pass data extraction within human-in-the-loop workflows with explicit oversight, and performance was most reliable within clearly defined workflows.
Abstract
Objectives
Systematic literature reviews (SLRs) are foundational to evidence-based medicine, including health technology assessment (HTA) and health economics and outcomes research (HEOR). Generative artificial intelligence (GenAI) tools are increasingly used in SLR workflows, yet no good practice guidance exists. This ISPOR Task Force report provides evidence-informed recommendations for responsible GenAI use across core SLR tasks.
Methods
A PRISMA-adapted rapid evidence assessment identified 115 empirical studies evaluating GenAI in SLR tasks published between November 2022 and July 2025. Findings were synthesized qualitatively across seven tasks. A structured task-level assessment framework spanning eight domains informed good practice recommendations, derived through Task Force consensus among experts in SLR methodology, HTA, AI development, bioethics, and regulatory science.
Results
Evidence supported GenAI use for high-recall title/abstract screening and structured first-pass data extraction within human-in-the-loop workflows with explicit oversight. Autonomous deployment was not supported. Evidence for full-text screening, qualitative synthesis, and report writing was more conditional, depending on workflow design and oversight, while risk of bias assessment showed the lowest readiness. End-to-end autonomous SLR generation was not recommended. Performance was most reliable within clearly defined workflows, with pre-specified rules for flagging records and explicit human review and conflict-resolution processes.
Conclusions
Based on current evidence, GenAI can augment, but not replace, human expertise in SLRs. Responsible use requires evaluating GenAI suitability for each review task, retaining human accountability at all decision points, and documenting AI use as a core methodological component. Because GenAI evolves rapidly, these recommendations reflect evidence through July 2025 and warrant periodic updating.