Palavras-chave
Eletrocardiograma
Sistemas inteligentes
Máquinas de aprendizagem extrema
Como Citar
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Resumo
Objetivo: Analisar, com base em publicações recentes (2015–2025), a eficácia da Inteligência Artificial (IA) na interpretação de eletrocardiogramas (ECGs) para fins diagnósticos. Revisão bibliográfica: Os estudos analisados demonstram que a IA pode transformar o ECG em uma ferramenta poderosa para triagem e predição, ao identificar padrões subclínicos que muitas vezes não são detectados por métodos convencionais. A incorporação de algoritmos de aprendizado de máquina e redes neurais profundas tem aumentado significativamente a precisão e a agilidade na detecção de anormalidades cardíacas. Destaca se, ainda, a importância da alfabetização em inteligência artificial (IA) e machine learning (ML) por parte dos profissionais de saúde, visando sua implementação clínica segura e eficaz. Em muitos contextos, os modelos baseados em IA apresentaram desempenho comparável ou superior ao de especialistas humanos, ampliando a aplicabilidade do ECG em ambientes clínicos diversos. Considerações finais: Embora desafios como validação externa, privacidade de dados e interpretabilidade dos algoritmos ainda persistam, o uso da IA na análise de ECGs mostra grande potencial para melhorar o diagnóstico precoce, a estratificação de risco e a tomada de decisão em cardiologia preventiva.
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