This paper presents a novel AI-powered diagnostic system for electronics troubleshooting that combines computer vision and large language models in a portable, real-time device. The system achieves 95. 3% component identification accuracy and reduces diagnostic time by 42% while lowering error rates from 18% to 3%. Through field validation across 12 repair facilities and 342 repair cases, we demonstrate the practical viability of AI-assisted diagnostics for electronics technicians. Key contributions include specialized diagnostic modes for different component types, continuous monitoring capability, and empirical validation of human-AI collaboration in technical work. The complete system costs 350-450 and achieves ROI within two weeks of deployment. Keywords: artificial intelligence, computer vision, circuit board diagnostics, large language models, electronics repair, human-AI collaboration, real-time analysis, embedded systems
harshad Khetpal (2026) studied this question.