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Artificial intelligence in clinical decision support and outcome prediction - applications in stroke.
Yeo, Melissa; Kok, Hong Kuan; Kutaiba, Numan; Maingard, Julian; Thijs, Vincent; Tahayori, Bahman; Russell, Jeremy; Jhamb, Ashu; Chandra, Ronil V; Brooks, Mark; Barras, Christen D; Asadi, Hamed.
Afiliação
  • Yeo M; School of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
  • Kok HK; Interventional Radiology Service, Department of Radiology, Northern Health, Melbourne, Victoria, Australia.
  • Kutaiba N; School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia.
  • Maingard J; Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia.
  • Thijs V; School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia.
  • Tahayori B; Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia.
  • Russell J; Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia.
  • Jhamb A; Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.
  • Chandra RV; Department of Neurology, Austin Health, Melbourne, Victoria, Australia.
  • Brooks M; Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.
  • Barras CD; IBM Research Australia, Melbourne, Victoria, Australia.
  • Asadi H; Department of Neurosurgery, Austin Hospital, Melbourne, Victoria, Australia.
Article em En | MEDLINE | ID: mdl-34050596
ABSTRACT
Artificial intelligence (AI) is making a profound impact in healthcare, with the number of AI applications in medicine increasing substantially over the past five years. In acute stroke, it is playing an increasingly important role in clinical decision-making. Contemporary advances have increased the amount of information - both clinical and radiological - which clinicians must consider when managing patients. In the time-critical setting of acute stroke, AI offers the tools to rapidly evaluate and consolidate available information, extracting specific predictions from rich, noisy data. It has been applied to the automatic detection of stroke lesions on imaging and can guide treatment decisions through the prediction of tissue outcomes and long-term functional outcomes. This review examines the current state of AI applications in stroke, exploring their potential to reform stroke care through clinical decision support, as well as the challenges and limitations which must be addressed to facilitate their acceptance and adoption for clinical use.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article