چگونه هوش مصنوعي مي تواند روش ارائه خدمات در اورژانس را تغيير دهد: مقاله مروري
کد: G-1040
نویسندگان: Mansoureh Fatahi * ℗, مسعود مایل
زمان بندی: زمان بندی نشده!
برچسب: سیستم های تصمیم یار بالینی
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Background: Emergency Departments (EDs) play a pivotal role in healthcare systems, often dealing with high patient volumes and critical cases under time pressure. Artificial Intelligence (AI) has emerged as a transformative tool capable of optimizing healthcare delivery. This review explores how AI technologies can influence ED workflows, improve clinical decision-making, and enhance patient outcomes. Methods: A narrative review approach was used to evaluate and synthesize current literature on AI applications across various aspects of ED operations. Key areas examined include triage, diagnostic interpretation (e.g., ECG, X-rays, CT scans), risk prediction, resource utilization, and workflow optimization. Databases and published peer-reviewed studies were reviewed, focusing on clinical performance, diagnostic accuracy, and implementation feasibility. Results: AI has shown promising applications in: • Triage: AI tools improve triage accuracy, reduce errors, and support the early identification of critically ill patients. • ECG Interpretation: AI algorithms outperform clinicians in detecting rhythm abnormalities, myocardial infarction, and other cardiac pathologies. • Medical Imaging: Deep learning models enhance diagnostic accuracy in interpreting plain X-Rays and CT scans. • Ultrasound: AI assists novice users, improves diagnostic accuracy in cardiac and lung assessments, and supports real-time decision-making. • Risk Prediction & Metrics: AI enables early prediction of ED volume, hospital admissions, disease severity, mortality, and patient length of stay. Conclusion: AI offers substantial benefits in augmenting emergency care, particularly in resource-limited settings. However, challenges such as data privacy, algorithmic bias, lack of clinical validation, and integration barriers need to be addressed. Transparent, explainable, and ethical AI deployment is essential for clinician trust and patient safety. Future work should focus on prospective trials and real-world integration.
کلمات کلیدی
AI, ED, Metrics, ECG, Triage, Imaging
