Artificial Intelligence in Emergency Medicine: Enhancing Decision-Making and Patient Outcomes

• DOI: https://doi.org/10.22533/at.ed.15941032406121

• Palavras-chave: Artificial intelligence; Emergency medicine; Predictive analytics; Triage systems; Critical care.

• Keywords: Artificial intelligence; Emergency medicine; Predictive analytics; Triage systems; Critical care.

• Abstract:

  INTRODUCTION Artificial intelligence has emerged as a transformative tool in emergency medicine, enhancing diagnostic precision, triage processes, and treatment strategies. By leveraging advanced algorithms and machine learning, AI systems process vast datasets in real time, addressing challenges inherent to high-pressure environments. These technologies have proven particularly effective in identifying critical conditions, predicting patient outcomes, and optimizing resource allocation, underscoring their potential to improve efficiency and patient care.
  OBJETIVE The main objective of this work was to evaluate the role of artificial intelligence as a decision support tool in medical emergencies, focusing on its impact on diagnostic accuracy, triage efficiency, and treatment optimization in critical care settings.
  METHODS This is a narrative review which included studies in the MEDLINE – PubMed (National Library of Medicine, National Institutes of Health), COCHRANE, EMBASE and Google Scholar databases, using as descriptors: “Artificial intelligence in emergency medicine” OR “Decision support systems in acute care” OR Machine learning for medical diagnostics” OR “AI-based triage and prioritization” OR “Predictive analytics in critical care” in the last  years.
  RESULTS AND DISCUSSION The application of AI in emergencies has demonstrated significant advancements across various domains. AI-driven tools reduce diagnostic errors, facilitate early detection of sepsis, and improve cardiac event management. Predictive analytics aid in triage prioritization and resource distribution during mass casualty incidents. However, challenges persist, including algorithmic bias, data interoperability issues, and the need for clinician acceptance. Addressing these limitations is critical for widespread adoption and optimal performance of AI systems.
  CONCLUSION AI is poised to revolutionize emergency medicine, but ethical, technical, and economic barriers must be addressed to fully harness its potential. By improving decision-making and patient outcomes, AI enhances the overall quality of emergency care. Continued research, clinician training, and policy development will be essential in ensuring the integration of AI technologies into emergency healthcare systems, paving the way for a more efficient and effective future in acute medical care.