MSR1 Using Artificial Intelligence Methods for Systematic Review in Health Sciences: A Systematic Review

Aymeric Blaizot, Sajesh Veettil, Pantakarn Saidoung, Carlos Moreno-García, Nirmalie Wiratunga, Magaly Aceves Martins, NM Lai, Nathorn Chaiyakunapruk

Research output: Contribution to journalAbstractpeer-review


Objectives: The exponential increase in published articles makes a thorough and
expedient review of literature increasingly challenging. Artificial Intelligent (AI)
platforms have been developed to attempt to address this problem, with them
progressively being incorporated into practice. This review delineated the common automated tools and platforms that employ AI approaches and evaluated the
reported benefits and challenges in using such methods.

Methods: A search was
conducted in 4 databases (Medline, Embase, Cochrane database of systematic reviews, and Epistemonikos) up to April 2021 for systematic reviews and other
related reviews implementing AI methods. To be included, an AI-assisted review
must use any form of AI method, including machine learning, deep learning, neural
network, or any other applications that are used to enable the full or semiautonomous performance of one or more stages in the development of evidence

Results: From a total of 4911 records identified, 87 articles underwent
full-text screening, and 12 reviews were included. These reviews used nine
different tools to implement 15 different AI methods. Eleven methods were used in
the screening stages of the review (73%). The rest were divided: two in data
extraction (13%) and two in risk of bias assessment (13%). The ambiguous benefits
of the data extractions, combined with the reported advantages from 10 reviews,
indicate that AI platforms have taken hold with varying success in evidence synthesis. The results are qualified by the reliance on the self-reporting of the review

Conclusions: Extensive human validation still appears required at this
stage in the implementation of AI/ML methods, though further evaluation is
required to define the overall contribution of such platforms in enhancing efficiency and quality in evidence synthesis.
Original languageEnglish
Pages (from-to)S517
Number of pages1
JournalValue in Health
Issue number7
Early online date25 Jun 2022
Publication statusPublished - 1 Jul 2022
EventISPOR 2022: The Future of HEOR in Patient-Driven Digital Healthcare Systems - Washington DC , United States
Duration: 10 May 202218 May 2022


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