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Presents an AI diagnostic system that improves component identification and diagnostic speeds in electronics repair, suggesting enhanced technician efficiency.
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.
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harshad Khetpal (2026) studied this question.
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