Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

Abstract

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic’s main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID‐19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

A State-of-the-Art Review of the BWM Method and Future Research Agenda

“…Thanks to the bibliometric method, current research trends are determined, the most prolific authors, countries, and organizations are pictured, and the most preferred journals are revealed. For performing in-depth analyses, the superiorities of VOSviewer-based bibliometrics are that it is easy to understand, does not require professionalism, and is open source (Alamoodi et al, 2023). Thereby, the present research adopts VOSviewer-based bibliometrics.…”

“…Given that BWM-based research has lasted for nearly a decade and is gaining more and more scholars' attention, a comprehensive overview of this field is required to seek the BWM development path, identify knowledge gaps, and establish a future research agenda. Bibliometric analysis, which has gained remarkable popularity by the scientific society, especially with the technological advances in recent years, is a meticulous, effective, and favored technique utilized to obtain and analyze large volumes of scientific data (Alamoodi et al, 2023). Not surprisingly, the bibliometric analysis has been employed in some well-known methods in MCDM, including AHP (Yu et al, 2021), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) (Zyoud & Fuchs-Hanusch, 2017), MACBETH (Ferreira & Santos, 2021), Data Envelopment Analysis (DEA) (Lampe & Hilgers, 2015), Decision Making Trial and Evaluation Laboratory (DEMATEL) (Koca & Yıldırım, 2021), and Multi-Objective Optimization on the basis of a Ratio Analysis plus the full Multıplicative form (MULTIMOORA) (Hafezalkotob et al, 2019), wherein the implementation of bibliometric is relatively detailed, i.e., analyzing publications regarding the structural relationships between journals, authors, countries, institutions, keywords, etc.…”

Impact of Artificial Intelligence and Internet of Things on Performance Management: A Systematic Review

“…The study examines essential trust-establishing mechanisms and ways of IoT security, privacy, and reliability enhancement to facilitate the development and uptake of IoT deployments. [22] conducted a systematic review of MCDM (multi-criteria decision-making) approaches in COVID-19 medical cases applying AI-driven decision support systems (DDS) in healthcare decision making. The paper discusses the problems, incentives, and ideas for the combination of MCDM approaches with AI in order to get better medical outcomes and decisions making.…”

Comparative Analysis of Telecommunications Infrastructure Resilience in BRICS Nations: An Integrated MCDM Approach

“…However, the success of this sector is contingent on its ability to adapt, evolve, and meet the increasing demands of a digitally connected world [9]. The path to building and maintaining robust telecommunications infrastructure in emerging economies is fraught with challenges [10]. These risks range from natural calamities like earthquakes and floods, which can devastate physical infrastructure, to technological challenges, such as the need for constant upgrades and protection against cyber threats [11].…”