Scholarlink

Biomedical waste refers to pathogenic or infectious waste generated by healthcare facilities, medical centers, and research institutions. In the current COVID-19 era, the use of personal protective equipment, testing kits, and surgical facemasks has become essential. The improper disposal of this new biomedical waste poses significant global concerns for public health and environmental sustainability. It can lead to the transmission of infections and environmental contamination, while handled inappropriately, particularly in developing countries. Effective management of this waste requires careful segregation, collection, and recycling to mitigate risks. While established protocols exist, public understanding of disposal methods is often lacking, highlighting the need for a systematic approach that involves thorough planning and evaluation. The use of artificial intelligence (AI) and the Internet of Things (IoT) for material recognition and robotic automation in solid waste management has been extensively studied. AI offers various machine learning methods, such as artificial neural networks (ANN), adaptive neurofuzzy inference systems (ANFIS), and support vector machines (SVM), to analyze data, make decisions, and perform tasks that typically require human intelligence. While AI continues to advance, the IoT presents significant opportunities in waste management by addressing critical challenges such as proper segregation, tracking, disposal, and compliance with regulatory standards. Despite the potential for a more digitalized future for waste collection, sorting, and recycling, the current level of digitalization among waste management firms remains unclear. Additionally, the application of AI and IoT in biomedical waste management is not as widespread as in other areas of environmental engineering. Therefore, this chapter will focus on the management of biomedical waste and explore the potential use of artificial intelligence, robotic automation, and the Internet of Things to efficiently oversee the entire process, from collection and sorting to recycling.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial Intelligence and Internet of Things: Collaborative Steps for Next-Generation Biomedical Waste Management

  • 章节

  • 发表日期：

    2026年

查看全文
  • Swati Shrivastava,
  • Nimisha Jadon,
  • Shaily Varshney,
  • Keisham Radhapyari,
  • Bayartungalag Batsaikhan

摘要

Biomedical waste refers to pathogenic or infectious waste generated by healthcare facilities, medical centers, and research institutions. In the current COVID-19 era, the use of personal protective equipment, testing kits, and surgical facemasks has become essential. The improper disposal of this new biomedical waste poses significant global concerns for public health and environmental sustainability. It can lead to the transmission of infections and environmental contamination, while handled inappropriately, particularly in developing countries. Effective management of this waste requires careful segregation, collection, and recycling to mitigate risks. While established protocols exist, public understanding of disposal methods is often lacking, highlighting the need for a systematic approach that involves thorough planning and evaluation. The use of artificial intelligence (AI) and the Internet of Things (IoT) for material recognition and robotic automation in solid waste management has been extensively studied. AI offers various machine learning methods, such as artificial neural networks (ANN), adaptive neurofuzzy inference systems (ANFIS), and support vector machines (SVM), to analyze data, make decisions, and perform tasks that typically require human intelligence. While AI continues to advance, the IoT presents significant opportunities in waste management by addressing critical challenges such as proper segregation, tracking, disposal, and compliance with regulatory standards. Despite the potential for a more digitalized future for waste collection, sorting, and recycling, the current level of digitalization among waste management firms remains unclear. Additionally, the application of AI and IoT in biomedical waste management is not as widespread as in other areas of environmental engineering. Therefore, this chapter will focus on the management of biomedical waste and explore the potential use of artificial intelligence, robotic automation, and the Internet of Things to efficiently oversee the entire process, from collection and sorting to recycling.