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It is argued that realizing the full potential of AI in instrumentation requires a balanced approach that combines technological innovation with robust governance frameworks, human oversight, and rigorous uncertainty quantification.
This article provides a comprehensive overview of AI-empowered intelligent instrumentation, with a focus on three representative application paradigms: automatic meter reading, fault diagnosis for predictive maintenance, and sensor calibration with drift compensation.
The integration of artificial intelligence into instrumentation and measurement systems has emerged as a transformative force across industrial, environmental, and scientific domains. This article provides a systematic overview of AI applications in instrumentation, encompassing intelligent sensor data processing, predictive maintenance, automated meter reading, and fault diagnosis. Drawing upon recent advances documented in the literature, we examine the methodological landscape ranging from conventional machine learning to deep learning and large language models. Key benefits include enhanced measurement accuracy through intelligent compensation, reduced downtime through predictive maintenance, and improved operational efficiency through automation. However, significant challenges persist, including the blackbox nature of AI models, data scarcity, uncertainty quantification, and the gap between laboratory performance and realworld deployment. We argue that the future of intelligent instrumentation lies in hybrid approaches that integrate datadriven AI with conventional modeldriven methods, thereby combining the pattern recognition capabilities of AI with the interpretability and rigor of physicsbased models.