AI for Medical Waste Management: A Bibliometric Analysis

Mohamed Laabidi () and Said Gattoufi
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Mohamed Laabidi: Université de Tunis, Institut Supérieur de Gestion de Tunis, Laboratoire SMART LR11ES03
Said Gattoufi: Université de Tunis, Institut Supérieur de Gestion de Tunis, Laboratoire SMART LR11ES03

A chapter in Advanced Data Analytics, Machine Learning and AI in Business, 2026, pp 74-91 from Springer

Abstract: Abstract Medical Waste Management (MWM) represents a major challenge for healthcare systems worldwide due to many factors, including the hazardous nature and environmental impact of medical waste. Traditional methods have proved inefficient and unsustainable. In recent years, Artificial Intelligence (AI) in particular has emerged as a revolutionary and innovative solution, notably through the integration of technologies such as machine learning, computer vision and predictive analytics, aimed at improving waste collection, classification and treatment processes. This study aims to provide a comprehensive bibliometric analysis of scientific research relating to AI applications in MWM. A total of 692 publications indexed in SCOPUS between 1978 and 2025 were examined, using the PRISMA methodology to ensure rigor, accuracy and transparency. Bibliometrix and Biblioshiny tools were used to analyze research trends, identify key authors and institutions, and map international collaboration networks. The results showed that the main themes addressed were sustainability, efficiency and improved data-driven decision-making. The COVID-19 pandemic also had a significant impact on the acceleration of research development in the two core areas of scientific production: “artificial intelligence” and “medical waste management”. The study confirmed the predominance of China, the USA and India in research in this field. It also revealed gaps and prospects for future research, in particular to help low- and middle-income countries adopt research in this field at local level and seek to implement intelligent and sustainable solutions in the healthcare sector in general and in MWM in particular.

Keywords: Artificial intelligence; medical waste management; efficiency; bibliometric analysis; machine learning; sustainability (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-23493-3_5

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