Abstract
Healthcare systems are both highly vulnerable to climate change and significant contributors to greenhouse gas emissions. Globally, healthcare accounts for approximately 4–5% of total emissions. Within this footprint, clinical waste management represents a carbon-intensive yet under-optimised operational domain. In England alone, the National Health Service (NHS) generates more than 150,000 tonnes of clinical waste annually, much of which is treated through high-emission processes such as incineration. Artificial intelligence (AI) has emerged as a potential enabler of efficiency and sustainability across healthcare operations, including waste segregation, logistics optimisation, treatment pathway selection, and compliance monitoring. However, existing research remains largely technology-centric and under-theorised from a management and organisational perspective. Moreover, the carbon footprint of AI systems, particularly deep learning models and cloud-based infrastructures, raises important governance and sustainability concerns. This research critically reviews AI-enabled clinical waste management through a sustainable business lens. It argues that AI should be conceptualised as a conditional enabler of net-zero healthcare rather than a direct decarbonisation tool. Its sustainability contribution depends on organisational readiness, governance arrangements, carbon-aware deployment strategies, and lifecycle assessment.