Artificial intelligence in clinical decision support and outcome prediction – applications in stroke

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 informationboth clinical and radiologicalwhich 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.

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit

“…The increasing integration of artificial intelligence and healthcare will also lead to more questions of government regulation, privacy, bias, and ethics. The government will need new regulatory frameworks to monitor these novel artificial intelligence models integrated into patient care as "Software as Medical Devices" to ensure these software are safe, valid, and efficacious and respect patient privacy [ 33 – 36 ]. Questions also remain about the liability and biases that could come with using this new technology.…”

Investigating the impact of innovative AI chatbot on post‐pandemic medical education and clinical assistance: a comprehensive analysis

“…Researchers in China compared treatment recommendations proposed by IBM Watson, a system that used natural language processing and machine learning to evaluate data, with actual clinical decisions by oncologists, finding high concordance with many types of cancer 18 . Various studies have also explored the employment of computer‐aided systems in early stroke detection, 19 with some automated tools demonstrating the detection time of CT head abnormalities to be on par with, or even faster than radiologists 20 . Rather than being designated for narrow and esoteric purposes, the advantage of ChatGPT and other similar LLMs is that they are capable of providing answers to a wide range of queries, even within the specialized field of medicine.…”

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

“…Meanwhile, within the 90-minute time window after the onset of middle cerebral artery (MCA) occlusion, both the high-density MCA sign and the Sylvian MCA point sign are obvious signs of NCCT and are among the earliest visible signs of ischemia [ 63 ]. MRI can identify abnormal lesions in the acute stage of ischemic stroke [ 64 ]. DWI is gradually being recognized as the gold standard for the diagnosis of acute ischemic stroke, with a sensitivity of 73%-92% for hyperacute ischemic stroke detection within 3 hours of onset, and it detects deficiency beyond 6 hours after onset [ 48 ].…”

“…The volumes in the ischemic core (irreversibly damaged tissue) and penumbra (potentially salvageable ischemic tissue) are of great significance for the outcomes in AIS patients. However, the manual segmentation of the ischemic core and penumbra is a time-consuming and laborious mission, with inconsistency across raters [ 64 ]. Both the ischemic core and the penumbra area are irregular in shape due to the time from symptom onset, vessel occlusion site, and collateral status.…”