Sürdürülebilir Kalkınma Amaçları

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• Derleme MakaleYayıncıda açık erişim2025Scopus SJRQ1/r/kostu/10667
  Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review

  Farrokh, Mehrdad; Fallahian, Amir H.; Rahmani, Erfan; Aghajan, Ali; Alipour, Morteza; Khouzani, Parisa Jafari; Hezarani, Hossein Boustani; Sabzehie, Hamed; Pirouzan, Mohammad; Pirouzan, Zahra

  Artificial intelligence (AI) systems have witnessed notable advancements, revolutionizing various fields of research and medicine. Specifically, advancements of AI and the rapid growth of machine learning hold immense potential to significantly impact emergency medicine. This narrative review aimed to summarize AI applications in prehospital emergency care, emergency radiology, triage and patient classification, emergency diagnosis and interventions, pediatric emergency care, trauma care, outcome prediction, as well as the legal and ethical challenges and limitations of AI use in emergency medicine. A comprehensive literature search was conducted in Web of Science, Scopus, and Medline using a wide range of artificial intelligence and machine learning-related keywords combined with terms related to emergency medicine to identify relevant published studies. The findings show that AI-powered tools can assist clinicians in emergency departments in improving the management of prehospital emergency care, emergency radiology, triage, emergency department workflow, complex diagnoses, treatment, clinical decision-making, pediatric emergency care, trauma care, and the prediction of admissions, discharges, complications, and outcomes. However, the majority of these applications have been reported in retrospective studies, whereas randomized controlled trials (RCTs) are essential to determine the true value of AI in emergency settings. These applications can serve as effective tools in emergency departments when they are continuously supplied with high-quality real-time data and are adopted through collaboration between skilled data scientists and clinicians. Implementing these AI-assisted tools in emergency departments requires adequate infrastructure and machine learning operation systems. Since emergency medicine involves various clinical decision-making scenarios based on classifications, flowcharts, and well-structured approaches, future well-designed prospective studies are necessary to achieve the goal of replacing conventional methods with new AI and machine learning techniques.

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  AtıfScopus10·WoS8·OpenAlex5

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• Derleme MakaleYayıncıda açık erişim2024Scopus SJRQ1/r/kostu/10726
  Monkeypox: A Comprehensive Review of Virology, Epidemiology, Transmission, Diagnosis, Prevention, Treatment, and Artificial Intelligence Applications

  Rahmani, Erfan; Bayat, Ziba; Farrokhi, Mehrdad; Karimian, Shiva; Zahedpasha, Reza; Sabzehie, Hamed; Poor, Sepehr Ramezani; Khouzani, Parisa Jafari; Aminpour, Solmaz; Karami, Mohammad

  Monkeypox (Mpox), an uncommon zoonotic Orthopoxvirus, is commonly manifested by blisters on the skin and has a mortality rate of approximately 0-10%. Approximately two decades after the cessation of global smallpox vaccination, the number of confirmed cases of Mpox has been growing, making it the most common Orthopoxvirus infection. Therefore, in this narrative review, we aimed to shed light on recent advancements in the pathophysiology, transmission routes, epidemiology, manifestations, diagnosis, prevention, and treatment of Mpox, as well as the application of artificial intelligence (AI) methods for predicting this disease. The clinical manifestations of Mpox, including the onset of symptoms and dermatologic characteristics, are similar to those of the infamous smallpox, but Mpox is clinically milder. Notably, a key difference between smallpox and Mpox is the high prevalence of lymphadenopathy. Human-to-human, animal-to-human, and animal-to-animal transmission are the three main pathways of Mpox spread that must be considered for effective prevention, particularly during outbreaks. PCR testing, as the preferred method for diagnosing Mpox infection, can enhance early detection of new cases and thereby improve infection control measures. JYNNEOS and ACAM2000 are among the vaccines most commonly recommended for the prevention of Mpox. Brincidofovir, Cidofovir, and Tecovirimat are the primary treatments for Mpox cases. Similar to other viral infections, the best approach to managing Mpox is prevention. This can, in part, be achieved through measures such as reducing contact with individuals displaying symptoms, maintaining personal safety, and adhering to practices commonly used to prevent sexually transmitted infections.

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  AtıfScopus11·WoS8·OpenAlex7

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