Advanced Diagnostic & Interventional Radiology Research Center | Challenges, and Future Directions of Artificial telligence

Advanced Diagnostic & Interventional Radiology Research Center | Challenges, and Future Directions of Artificial telligence
| Dec 10 2025
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Advanced Diagnostic & Interventional Radiology Research Center

COVID-19 pandemic 

During the COVID-19 pandemic, the Radiology Research Center at Tehran University of Medical Sciences continued its research activities despite the challenges posed by the increased demand for CT scans of COVID-19 patients and the necessity of adhering to strict health protocols. This center played a crucial role in improving medical imaging techniques, optimizing diagnostic protocols, and advancing technologies related to CT scan image analysis.

Faculty members, researchers, and staff remained committed to ensuring the safety and well-being of healthcare professionals and patients while actively engaging in imaging data analysis, developing artificial intelligence algorithms for faster disease detection, publishing scientific articles, and presenting their findings at international conferences. These efforts aimed to enhance diagnostic accuracy, improve treatment processes, and alleviate pressure on healthcare systems.

 

Key achievements of the Radiology Research Center during the COVID-19 pandemic include:


✔️ Development and optimization of lung imaging protocols for faster and more accurate COVID-19 diagnosis
✔️ Implementation of artificial intelligence technologies for automated CT scan analysis and reduced diagnosis time
✔️ Publication of high-impact research articles on innovative imaging methods for COVID-19 patients
✔️ Participation in national and international projects focused on COVID-19 diagnosis and patient management

The center remains dedicated to advancing research in medical imaging and continues to contribute as a leading scientific institution in improving the quality of diagnostic and therapeutic services.

 

Some of the center's significant achievements during the pandemic include:

 

  • Release Date : Jul 15 2025 - 07:09
  • : 59
  • Study time : 1 minute(s)

Current Applications, Challenges, and Future Directions of Artificial telligence in Emergency Medicine: A Narrative Review

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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.

  • Article_DOI : 10.22037/aaemj.v13i1.2712
  • Author(s) : mehrdad farrokhi
  • News Group : research,research article
  • News Code : 300923
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