American Journal of Advanced Multidisciplinary Research and Innovation

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Cross-Disciplinary Applications of Computer Vision in Healthcare and Engineering

Author(s) Tommi S. Jaakkola
Country United States
Abstract Computer Vision (CV), a major branch of Artificial Intelligence (AI), has rapidly evolved into a transformative technology with significant applications across healthcare and engineering. By enabling machines to interpret, analyse, and understand visual information from images, videos, and sensor data, computer vision has revolutionised diagnostic medicine, medical imaging, intelligent manufacturing, structural inspection, robotics, autonomous systems, and industrial automation. Recent advances in Deep Learning (DL), Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), edge computing, Internet of Things (IoT), cloud computing, and explainable AI have further enhanced the accuracy, efficiency, and scalability of computer vision systems in multidisciplinary environments.
This study investigates the cross-disciplinary applications of computer vision in healthcare and engineering through a qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, international technical reports, industrial case studies, and healthcare research publications. The study explores computer vision applications in disease diagnosis, medical image analysis, surgical assistance, patient monitoring, quality inspection, predictive maintenance, intelligent robotics, infrastructure monitoring, autonomous vehicles, and precision manufacturing.
The findings indicate that computer vision significantly improves diagnostic accuracy, operational efficiency, decision support, automation, safety, and predictive maintenance across both healthcare and engineering domains. AI-powered image recognition models facilitate early disease detection, automated defect inspection, intelligent robotic control, and real-time monitoring of complex systems. Integration with IoT devices, Digital Twins, edge computing, cloud platforms, and explainable AI further enhances system reliability and real-time decision-making.
Despite these benefits, challenges including data privacy, cybersecurity, limited annotated datasets, algorithmic bias, model interpretability, computational requirements, and regulatory compliance continue to hinder widespread implementation. The study concludes that interdisciplinary collaboration, responsible AI governance, and continued technological innovation are essential for maximising the societal and industrial benefits of computer vision technologies.
Keywords Computer Vision, Artificial Intelligence, Healthcare, Engineering, Deep Learning, Medical Imaging, Intelligent Manufacturing, Robotics, Image Processing, Explainable AI.
Field Engineering
Published In Volume 3, Issue 1, January-February 2021
Published On 2021-01-17
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