Medical-Waste Chain: A Medical Waste Collection, Classification and Treatment Management by Blockchain Technology

Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care

The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.

An innovative medical waste management system in a smart city using XAI and vehicle routing optimization

Background

The management of medical waste is a complex task that necessitates effective strategies to mitigate health risks, comply with regulations, and minimize environmental impact. In this study, a novel approach based on collaboration and technological advancements is proposed.

Methods

By utilizing colored bags with identification tags, smart containers with sensors, object recognition sensors, air and soil control sensors, vehicles with Global Positioning System (GPS) and temperature humidity sensors, and outsourced waste treatment, the system optimizes waste sorting, storage, and treatment operations. Additionally, the incorporation of explainable artificial intelligence (XAI) technology, leveraging scikit-learn, xgboost, catboost, lightgbm, and skorch, provides real-time insights and data analytics, facilitating informed decision-making and process optimization.

Results

The integration of these cutting-edge technologies forms the foundation of an efficient and intelligent medical waste management system. Furthermore, the article highlights the use of genetic algorithms (GA) to solve vehicle routing models, optimizing waste collection routes and minimizing transportation time to treatment centers.

Conclusions

Overall, the combination of advanced technologies, optimization algorithms, and XAI contributes to improved waste management practices, ultimately benefiting both public health and the environment.

A hybrid machine learning-mathematical programming optimization approach for municipal solid waste management during the pandemic

This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-023-03354-2.

Analysis of COVID-19 waste management in Vietnam and recommendations to adapt to the 'new normal' period

Amid the 4th wave of COVID-19, Vietnam reopened its economy, which placed extra burdens on the COVID-19 waste management system. This study analyzed existing issues and recommended adaptations to secure appropriate management of COVID-19 waste under the 'new normal' pandemic period. Results showed changes in COVID-19 waste characteristics (e.g., rapid rise in waste generation, lower percentage of plastic) and multiple other issues (e.g., presence of COVID-19 waste in municipal waste, lack of temporary storage sites and local treatment capacity), along with greater waste-handling responsibilities placed on authorities and higher infection risks. To adapt to the 'new normal', introduction of separate handling routes and collaboration in waste treatment were recommended. Employing the network of pharmacies used for vaccination would require COVID-19 waste collection from scattered, small-scale sources as part of the waste management solution. Also, following the 4R initiatives (reduce, reuse, recycle, recovery) could help ease the burden on the country's waste system and provide additional opportunities to move towards a circular economy in the post-acute COVID-19 era. The findings should contribute to a safer co-existence with the virus through appropriate waste management in Vietnam and could be used to tackle waste problems in other developing countries.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10163-022-01563-x.