Artificial intelligence and the conduct of literature reviews

Wagner, Gerit; Lukyanenko, Roman; Paré, Guy

doi:10.1177/02683962211048201

Cited by 58 publications

(17 citation statements)

References 104 publications

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“…We find deeper insights into those articles to detect the diseases using advanced technology. While broader studies have already indicated healthcare and medical diagnosis adoption of different applications, none of them has focused on how to improve the literature review using a combination of traditional literature review and artificial intelligence (Kushwaha et al, 2021;Wagner et al, 2022;Xiao et al, 2018). This study endeavours to disclose to new and future researchers on how to conduct a smart literature investigation to obtain accurate insightful results.…”

Section: Contribution To the Theorymentioning

confidence: 99%

Extending artificial intelligence research in the clinical domain: a theoretical perspective

2022

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Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

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Section: Contribution To the Theorymentioning

confidence: 99%

Extending artificial intelligence research in the clinical domain: a theoretical perspective

2022

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“…While a synthesis does include substantiation in terms of the validity of the associated relations, the student would be tasked to manually identify a derived synthesis or fuse a subset of candidate syntheses into a derived synthesis. That is, artificially smart technologies could supplement the manual natural intelligence of a researcher, replacing the traditional labor component of collecting, reading, comprehending, and synthesizing the manual literature review with a cognified one (Wagner et al, 2022). The combination would support deeper and richer research exploration.…”

Section: Cognification In Educationmentioning

confidence: 99%

Cognification in Learning, Teaching, and Training

Kumar

Ally

Tsinakos

et al. 2022

CJLT

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Over the past decade, opportunities for online learning have dramatically increased. Learners around the world now have digital access to a wide array of corporate trainings, certifications, comprehensive academic degree programs, and other educational and training options. Some organizations are blending traditional instruction methods with online technologies. Blended learning generates large volumes of data about both the content (quality and usage) and the learners (study habits and learning outcomes). Correspondingly, the need to properly process voluminous, continuous, and often disparate data has prompted the advent of cognification. Cognification techniques design complex data analytic models that allow natural intelligence to engage artificial smartness in ways that can enhance the learning experience. Cognification is the approach to make something increasingly, ethically, and regulatably smarter. This article highlights how emerging trends in cognification could disrupt online education.

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“…Systematic reviews and meta-analyses are fundamental to supporting reproducibility and generalizability of research surrounding social and cultural aspects of human behavior, however, the process of extracting data from primary research is a labor-intensive effort, fraught with the potential for human error (see Pigott & Polanin, 2020;Yu et al, 2018). In contrast with the more defined standards that have evolved throughout the clinical research domain, within and across social sciences, substantial variation exists in research designs, reporting protocols, and even publication outlet standards (Davis et al, 2014;Short et al, 2018;Wagner et al, 2022). Notwithstanding that application of automation technologies in the social sciences could benefit from greater standardization of reporting protocols and terminology, understanding of the current state of (semi) automated extraction across these disciplines is largely speculative.…”

Section: Introductionmentioning

confidence: 99%

(Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review protocol

Legate

Nimon²

2023

F1000Res

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Background: An abundance of rapidly accumulating scientific evidence presents novel opportunities for researchers and practitioners alike, yet such advantages are often overshadowed by resource demands associated with finding and aggregating a continually expanding body of scientific information. Across social science disciplines, the use of automation technologies for timely and accurate knowledge synthesis can enhance research translation value, better inform key policy development, and expand the current understanding of human interactions, organizations, and systems. Ongoing developments surrounding automation are highly concentrated in research for evidence-based medicine with limited evidence surrounding tools and techniques applied outside of the clinical research community. Our objective is to conduct a living systematic review of automated data extraction techniques supporting systematic reviews and meta-analyses in the social sciences. The aim of this study is to extend the automation knowledge base by synthesizing current trends in the application of extraction technologies of key data elements of interest for social scientists. Methods: The proposed study is a living systematic review employing a partial replication framework based on extant literature surrounding automation of data extraction for systematic reviews and meta-analyses. Protocol development, base review, and updates follow PRISMA standards for reporting systematic reviews. This protocol is preregistered in OSF: (Semi)Automated Approaches to Data Extraction for Systematic Reviews and Meta-Analyses in Social Sciences: A Living Review Protocol on August 14, 2022. Conclusions: Anticipated outcomes of this study include: (a) generate insights supporting advancement in transferring existing reliable methods to social science research; (b) provide a foundation for protocol development leading to enhancement of comparability and benchmarking standards across disciplines; and (c) uncover exigencies that spur continued value-adding innovation and interdisciplinary collaboration for the benefit of the collective systematic review community.

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Artificial intelligence and the conduct of literature reviews

Cited by 58 publications

References 104 publications

Extending artificial intelligence research in the clinical domain: a theoretical perspective

Extending artificial intelligence research in the clinical domain: a theoretical perspective

Cognification in Learning, Teaching, and Training

(Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review protocol

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