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Abstract The rapid advancement of artificial intelligence (AI) has brought significant benefits across various domains, yet it has also led to increased energy consumption and environmental impact. This paper positions Green AI as a crucial direction for future research and development. It proposes a comprehensive framework for understanding, implementing, and advancing sustainable AI practices. We provide an overview of Green AI, highlighting its significance and current state regarding AI’s energy consumption and environmental impact. The paper explores sustainable AI techniques, such as model optimization methods, and the development of efficient algorithms. Additionally, we review energy-efficient hardware alternatives like tensor processing units (TPUs) and field-programmable gate arrays (FPGAs), and discuss strategies for designing and operating energy-efficient data centers. Case studies in natural language processing (NLP) and Computer Vision illustrate successful implementations of Green AI practices. Through these efforts, we aim to balance the performance and resource efficiency of AI technologies, aligning them with global sustainability goals.

The opioid crisis has led to an increased number of drug overdoses in recent years. Several approaches have been established to predict opioid prescription by health practitioners. However, due to the complex nature of the problem, the accuracy of such methods is not yet satisfactory. Dependable and reliable classification of opioid dependent patients from well-grounded data sources is essential. Majority of the previous studies do not focus on the users’ mental health association for opioid intake classification. These studies do not also employ the latest deep learning based techniques such as attention and knowledge distillation mechanism to find better insights. This paper investigates the opioid classification problem by using machine learning and deep learning based techniques. We used structured and unstructured data from the MIMIC-III database to identify intentional and unintentional intake of opioid drugs. We selected 455 patient instances and used traditional machine learning and deep learning to predict intentional and accidental users. We obtained 95% and 64% test accuracy to predict the intentional and accidental users from the structured and unstructured datasets, respectively. We also achieve a distilled knowledge based test accuracy of 76.44% from the integrated above two models. Our research includes an ablation analysis and new insights related to opioid patients are extracted.