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Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.
Fathi, Mobina; Eshraghi, Reza; Behzad, Shima; Tavasol, Arian; Bahrami, Ashkan; Tafazolimoghadam, Armin; Bhatt, Vivek; Ghadimi, Delaram; Gholamrezanezhad, Ali.
Afiliación
  • Fathi M; Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.
  • Eshraghi R; School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Behzad S; Student Research Committee, Kashan University of Medical Science, Kashan, Iran.
  • Tavasol A; Independent Researcher, Tehran, Iran.
  • Bahrami A; School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Tafazolimoghadam A; Student Research Committee, Kashan University of Medical Science, Kashan, Iran.
  • Bhatt V; Tehran University of Medical Science (TUMS), Tehran, Iran.
  • Ghadimi D; School of Medicine, University of California, Riverside, CA, USA.
  • Gholamrezanezhad A; School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Emerg Radiol ; 2024 Aug 27.
Article en En | MEDLINE | ID: mdl-39190230
ABSTRACT
Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplace efficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures, and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Emerg Radiol Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Emerg Radiol Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Estados Unidos