American Journal of Advanced Multidisciplinary Research and Innovation

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Cross-Disciplinary Applications of Artificial Intelligence in Engineering, Healthcare, and Environmental Sustainability

Author(s) Trevor Darrell
Country United States
Abstract Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, driving innovation across engineering, healthcare, and environmental sustainability. The integration of Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Computer Vision, Internet of Things (IoT), cloud computing, Digital Twin technology, robotics, edge computing, and Big Data Analytics has enabled intelligent automation, predictive modelling, and data-driven decision-making in multidisciplinary domains. In engineering, AI enhances smart manufacturing, predictive maintenance, structural health monitoring, intelligent design optimisation, and autonomous robotic systems. In healthcare, AI supports disease diagnosis, medical image analysis, precision medicine, telemedicine, wearable health monitoring, and clinical decision support. Environmental sustainability benefits from AI-enabled climate modelling, renewable energy forecasting, biodiversity conservation, pollution monitoring, precision agriculture, disaster prediction, and smart resource management. Collectively, these applications contribute to improved operational efficiency, resource optimisation, innovation, and sustainable development.
This study investigates the cross-disciplinary applications of Artificial Intelligence in engineering, healthcare, and environmental sustainability using a qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, international reports, government publications, and multidisciplinary case studies. The study analyses emerging AI technologies, integrated digital ecosystems, implementation frameworks, sector-specific applications, and future research opportunities. It also explores the role of explainable AI, blockchain, federated learning, cyber-physical systems, and Digital Twin technology in creating secure, transparent, and intelligent solutions.
The findings indicate that AI significantly enhances engineering productivity, healthcare quality, environmental resilience, and organisational decision-making through intelligent automation, predictive analytics, and real-time monitoring. AI-enabled systems improve equipment reliability, early disease detection, personalised healthcare, renewable energy integration, climate adaptation, smart city management, and sustainable industrial processes. However, widespread adoption faces challenges including cybersecurity threats, data privacy concerns, ethical AI governance, algorithmic bias, interoperability limitations, workforce skill gaps, regulatory complexity, and infrastructure constraints.
The study concludes that responsible AI governance, interdisciplinary collaboration, sustainable digital innovation, and human-centred technological development are essential for maximising the societal benefits of Artificial Intelligence while supporting resilient, inclusive, and sustainable global development.
Keywords Artificial Intelligence, Engineering, Healthcare, Environmental Sustainability, Machine Learning, Digital Twin, Internet of Things, Smart Technologies, Sustainable Development, Multidisciplinary Research.
Field Engineering
Published In Volume 3, Issue 2, March-April 2021
Published On 2021-03-14
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