Artificial intelligence in clinical decision support and outcome prediction - applications in stroke.

2021-05-28

Yeo, Melissa

Kok, Hong Kuan

Kutaiba, Numan

Maingard, Julian

Thijs, Vincent N

Tahayori, Bahman

Russell, Jeremy H

Jhamb, Ashu

Chandra, Ronil V

Brooks, Duncan Mark

Barras, Christen D

Asadi, Hamed

artificial intelligence

computer aided diagnosis

computers in radiology

decision support

machine learning

neuroradiology

outcome prediction

Stroke

Journal Article

0000-0001-5568-7303

0000-0003-4627-9847

0000-0003-1899-1909

0000-0003-2475-9727

10.1111/1754-9485.13193

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.

link

Journal of Medical Imaging and Radiation Oncology 2021; online first: 28 May

Journal of Medical Imaging and Radiation Oncology