International Journal of AI and Advanced Computing

Volume 1, Issue 3

Research Article Open Access

VISION-LINK AI Smart Glass: An Edge-Cloud Wearable AI System for Workflow Automation and Digital Inclusion in Emerging Economies

Abstract

The rapid advancement of Artificial Intelligence (AI), wearable computing, and Internet of Things (IoT) technologies has significantly transformed digital interactions across various sectors. However, the adoption of smart wearable technologies remains limited in many developing countries due to high acquisition costs, inadequate localization, and dependence on stable internet connectivity. This research presents the design, development, and deployment of the VISION-LINK AI Smart Glass, an intelligent wearable device specifically designed to address these challenges within resource-constrained environments. The proposed system integrates Artificial Intelligence, Natural Language Processing (NLP), Computer Vision, Speech Recognition, Edge Computing, and Cloud-based AI services to provide real-time language translation, hands-free workflow automation, intelligent documentation, object recognition, and voice-assisted interaction. The system architecture combines low-cost hardware components (ESP32-CAM, TP4056 power management, and OLED interface) with a secure software infrastructure to improve accessibility, productivity, and digital inclusion across healthcare, engineering, agriculture, and education. Quantitative evaluation demonstrates an offline inference latency of <1.45 seconds, object recognition accuracy of 92.4\%, and a 45\% reduction in power consumption compared to continuous cloud-streaming architectures.

Research Article Open Access

Rethinking Biological Categories: Nancy Cartwright on AI in Medicine

Abstract

Artificial Intelligence (AI) is now central to pharmaceutical research and clinical practice across the globe. It promises faster drug discovery and more precise care. Despite heavy investment, the clinical impact of AI in medicine remains limited in both high- and low-income settings. This paper argues that the limitation is philosophical, not only technical. Medicine continues to use disease categories built for 20th century regulation and billing. When AI systems are trained on these categories, they learn and scale the error. Drawing on Nancy Cartwright’s concept of “Evidence for Use” in her The Dappled World and on African and Asian critiques of universalism in medicine, I argue that a treatment’s capacity to work is local. It depends on the specific causal arrangement in which it was established. The paper uses conceptual analysis, drawing on Cartwright's philosophy of science and African and Asian philosophy of medicine, with hypertension as an illustrative case, focusing on salt-sensitive hypertension in West Africa and East Asia. Since AI learns from broad labels, it reproduces mismatch at scale. To make AI work in medicine, we must rethink biological categories. We must move from universal labels to mechanism-based categories grounded in evidence for use. The paper shows that AI bias is rooted partly in biological classification, not only in data quality. It concludes that philosophy is infrastructure. Without recalibrating categories, AI in medicine will be fast but not good, especially for patients in Africa and Asia.

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