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SYSTEMATIC REVIEW article

Front. Pharmacol.
Sec. Drugs Outcomes Research and Policies
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1454245
This article is part of the Research Topic Digital Health Innovations in Africa: Harnessing AI, Telemedicine, and Personalized Medicine for Improved Healthcare View all articles

How Much Can We Save by Applying Artificial Intelligence in Evidence Synthesis? Results from a Pragmatic Review to Quantify Workload Efficiencies and Cost Savings

Provisionally accepted
Seye Abogunrin Seye Abogunrin 1Jeffrey M Muir Jeffrey M Muir 2*Clarissa Zerbini Clarissa Zerbini 1Grammati Sarri Grammati Sarri 2
  • 1 Roche (Switzerland), Basel, Switzerland
  • 2 Cytel (United States), Cambridge, United States

The final, formatted version of the article will be published soon.

    Researchers are increasingly exploring the use of artificial intelligence (AI) tools in evidence synthesis, a labor-intensive, time-consuming, and costly effort. This review explored and quantified the potential efficiency benefits of using automated tools as part of core evidence synthesis activities compared with human-led methods. We searched the MEDLINE and Embase databases for Englishlanguage articles published between 2012 and November 14, 2023, and hand-searched the ISPOR presentations database (2020-2023) for articles presenting quantitative results on workload efficiency in systematic literature reviews (SLR) when AI automation tools were utilized. Data on efficiencies (time-and cost-related) were collected. We identified 25 eligible studies: 13 used machine learning, 10 used natural language processing, and once each used a systematic review automation tool and a non-specified AI tool. In 17 studies, a >50% time reduction was observed, with 5-to 6-fold decreases in abstract review time. When the number of abstracts reviewed was examined, decreases of 55%-64% were noted. Studies examining work saved over sampling at 95% recall reported 6-to 10-fold decreases in workload with automation. No studies quantified the economic impact associated with automation, although one study found that there was an overall labor reduction of >75% over manual methods during dual-screen reviews. AI can reduce both workload and create time efficiencies when applied to evidence gathering efforts in SLRs. These improvements can facilitate the implementation of novel approaches in decision making that consider the real-life value of health technologies. Further research should quantify the economic impact of automation in SLRs.

    Keywords: artificial intelligence, Systematic review, Evidence synthesis, efficiencies, machine learning

    Received: 24 Jun 2024; Accepted: 09 Jan 2025.

    Copyright: © 2025 Abogunrin, Muir, Zerbini and Sarri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jeffrey M Muir, Cytel (United States), Cambridge, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.