Description

• Title:
  The use of artificial intelligence in radiology: new possibilities for diagnostic imaging. A literature review
• Creator:
  Stawska, Weronika;
  Miłek, Magdalena;
  Kwaśniak, Ksenia;
  Foryś, Angelika;
  Banach, Mariola;
  Niemczyk, Anna;
  Ślusarczyk, Monika;
  Magierska, Agata;
  Kotowicz, Zuzanna;
  Kmiotek, Weronika
• Description:
  Introduction and purpose: Rapid advances in technology enable innovative solutions to be implemented in modern medicine, relieving healthcare workers by speeding up diagnosis and improving the quality of treatment. The subject of this review is Artificial Intelligence (AI), an innovative form of help in the daily practice of doctors. It offers the opportunity to relieve healthcare workers by accelerating the diagnosis process and improving the quality and effectiveness of treatment.  The aim of this paper is to present the current state of knowledge, assess the effectiveness of algorithms in the recognition and interpretation of abnormalities in medical images compared to specialists in radiology and discuss the challenges associated with the implementation of AI in various medical specialties. Brief description of the state of knowledge: Artificial intelligence, especially through machine learning and deep learning techniques, has found wide application in radiology. Many facilities around the world use advanced AI systems in the day-to-day work of radiologists, including the Mayo Clinic, Massachusetts General Hospital, and the University of Tokyo Hospital.  Summary: Studies show that while AI algorithms can perform worse than radiologists in some areas, they are at the forefront of others, especially in detecting subtle abnormalities. The effective implementation of artificial intelligence requires addressing regulatory, ethical, and training issues. Despite these challenges, artificial intelligence has the potential to play    a key role in the future of radiology and revolutionize medical practice, opening new perspectives and improving the quality of health care. 
• Object availability:
  free access
• Rights: https://creativecommons.org/licenses/by-nc-sa/4.0 ;
  Prawa autorskie (c) 2024 Weronika Stawska, Magdalena Miłek, Ksenia Kwaśniak, Angelika Foryś, Mariola Banach, Anna Niemczyk, Monika Ślusarczyk, Agata Magierska, Zuzanna Kotowicz, Weronika Kmiotek
• Date:
  2024.07.07
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Language:
  English
• Source:
  Quality in Sport; Tom 16 (2024); 52215;
  Quality in Sport; Vol. 16 (2024); 52215;
  2450-3118
• Format:
  application/pdf
• Publisher:
  University Center for Sports Research, Nicolaus Copernicus University, Torun, Poland
• Relation: https://apcz.umk.pl/QS/article/view/52215/39014
• Subject:
  Artificial Intelligence;
  medical imaging;
  radiology;
  deep learning;
  diagnostic algorithms
• Identifier: https://apcz.umk.pl/QS/article/view/52215
• Data provider:
  Akademicka Platforma Czasopism
• Can I use it?:
  seek permission
• Type:
  text