Artificial intelligence in clinical decision support and outcome prediction - applications in stroke.
Melissa YeoHong Kuan KokNuman KutaibaJulian MaingardVincent ThijsBahman TahayoriJeremy RussellAshu JhambRonil V ChandraMark BrooksChristen David BarrasHamed AsadiPublished in: Journal of medical imaging and radiation oncology (2021)
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.
Keyphrases
- artificial intelligence
- clinical decision support
- big data
- machine learning
- deep learning
- electronic health record
- atrial fibrillation
- healthcare
- palliative care
- end stage renal disease
- health information
- prognostic factors
- high resolution
- type diabetes
- newly diagnosed
- cerebral ischemia
- peritoneal dialysis
- intellectual disability
- skeletal muscle
- photodynamic therapy
- patient reported outcomes
- quality improvement
- mass spectrometry
- climate change
- loop mediated isothermal amplification
- fluorescence imaging