AI Integration in green labs to achieve the third SDG goal: Literature review on good health and well being - Ahmad Fauzi; Diani Fatmawati (Green and Tropical Laboratory for Sustainability, 2025)
Background: Artificial intelligence (AI) is increasingly applied in healthcare and sustainability-related domains to improve efficiency, health outcomes, and decision-making. However, its expanding use also raises critical concerns regarding environmental impact, ethical governance, and equitable access, necessitating a comprehensive synthesis of existing evidence. Objectives: This review aims to evaluate AI applications that improve sustainability and health outcomes, compare AI-based green laboratory practices against SDG 3, identify integration challenges, compare resource optimization strategies, and analyze interdisciplinary collaboration frameworks. Methods: A systematic analysis of 50 global studies using diverse AI methodologies, including machine learning, deep learning, IoT, and green computing was conducted, with a focus on empirical and conceptual evidence from clinical and research laboratories. Results: Findings reveal that AI significantly improves laboratory efficiency, diagnostic accuracy, and resource management, achieving measurable reductions in energy, water, and waste while advancing personalized healthcare. However, the environmental costs of AI, ethical issues such as algorithmic bias and equitable access, and limited scalability in low-resource settings remain critical challenges. Multisectoral collaboration and governance frameworks are crucial but underdeveloped for the responsible deployment of AI in green laboratories. Conclusion: Integrating AI innovation with sustainability goals requires balancing technological advancements with ethical and environmental imperatives. These insights inform a theoretical framework and practical strategies for optimizing AI integration in sustainable laboratory environments, supporting global health and well-being goals within defined temporal and geographic research boundaries.