Aplicação de inteligência artificial no diagnóstico de falhas automotivas: limitações e oportunidades sob uma perspectiva evolutiva

Abstract

The increasing complexity of electronic systems embedded in modern vehicles has driven the need for more advanced and efficient methods of automotive fault diagnosis. In this context, Artificial Intelligence (AI) emerges as a strategic tool for data analysis, pattern identification, and support for technical decision-making. This article aims to analyze, from a historical and evolutionary perspective, the application of AI in automotive diagnostics, highlighting its main technical limitations and future opportunities. The methodology adopted is based on a technical-scientific literature review and practical analysis from the perspective of a specialist dealership technician, with a focus on electronic systems and diagnostic tools. The results indicate that, although AI presents limitations related to data quality, model transparency, and integration with legacy systems, its potential for implementing predictive diagnostics and advanced technical assistance is significant. It is concluded that the synergy between human knowledge and intelligent systems will be decisive for the future of automotive maintenance.