Artificial intelligence in cardiopulmonary resuscitation: a systematic review
Inteligência artificial na ressuscitação cardiopulmonar: uma revisão sistemática
Diogenes de Oliveira Silva, Giorgio Pretto, Saul Dominici, Antonio Carlos Aguiar Brandão, Plínio da Cunha Leal
Abstract
Keywords
Resumo
A parada cardíaca continua sendo uma das principais causas de mortalidade e incapacidade neurológica, e seu manejo depende do reconhecimento rápido, de uma ressuscitação eficaz e da determinação precisa do prognóstico pós-parada. A inteligência artificial (IA) tem sido cada vez mais investigada como uma ferramenta de apoio a essas etapas da assistência. O objetivo desta revisão narrativa foi sintetizar as evidências recentes sobre as aplicações de IA na parada cardíaca, incluindo predição, reconhecimento, suporte durante a parada cardiorrespiratória, prognóstico, e risco de estratificação cardiovascular. Foram realizadas buscas nas bases de dados PubMed/MEDLINE, Google Scholar, EMBASE, Scopus, IEEE Xplore e ScienceDirect para estudos publicados de janeiro de 2000 a junho de 2025. Estudos que abordavam IA, aprendizado de máquina (machine learning) ou aprendizado profundo (deep learning) em contextos relacionados à parada cardíaca foram sintetizados descritivamente de acordo com a aplicação clínica, metodologia de IA e cenário de assistência. No total, 59 estudos foram incluídos. Seis categorias de aplicação foram identificadas: predição precoce de parada cardíaca ou deterioração clínica (n = 16); reconhecimento de parada cardíaca e ativação da resposta de emergência (n = 3); RCP intra-parada e suporte à desfibrilação (n = 6); determinação do prognóstico pós-parada cardíaca (n = 20); estratificação de risco para morte súbita cardíaca e arritmias malignas (n = 11); e modelos prognósticos cardiovasculares ou perioperatórios relacionados (n = 3). Abordagens baseadas em aprendizado profundo e redes neurais foram as metodologias mais frequentes (n = 35), seguidas por aprendizado de máquina tradicional (n = 18) e processamento de linguagem natural (n = 6). A IA mostrou-se particularmente promissora para predição baseada em ECG, trajetórias clínicas temporais, interpretação de chamadas de emergência, análises de forma de onda e desfibrilação, e prognóstico de mortalidade ou neurológico pós-parada. A IA apresenta grande potencial para apoiar as diversas etapas do cuidado na parada cardíaca, fornecendo ferramentas preditivas e de suporte à decisão. No entanto, as evidências atuais permanecem heterogêneas, uma vez que a maioria dos estudos depende de conjuntos de dados retrospectivos, populações selecionadas e validação externa ou prospectiva limitada.
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References
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Submitted date:
01/09/2026
Accepted date:
07/05/2026
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