National Randomized Controlled Trial of Virtual House Calls for Parkinson Disease
Neurol 89:1152-1161,1103, Beck, C.A.,et al, 2017
Artificial Intelligence and Machine Learning in Clinical Medicine, 2023
NEjM 388:1201-1208,1220, Haug,C.H. & Drazen,J.M., 2023
Rapid Implementation of Virtual Neurology in Response to the COVID-19 Pandemic
Neurol 94:1077-1087, Grossman, S.N.,et al, 2020
Reimagining Specialty Consultation in the Digital Age the Potential Role of Targeted Automatic Electronic Consultations
JAMA doi:10.1001/JAMA.2019.6607, Wachter, R.M.,et al, 2019
Automated DWI analysis can identify patients within the thrombolysis time window of 4.5 hours
Neurol 90:e1570-e1577, Wouters, A.,et al, 2018
A Fatal Case of Undiagnosed Candida Meningitis-Role of Computer-assisted Diagnosis
Neurologist 23:138-140, Finelli, P.F., 2018
Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy
Stroke 48:1416-1419, Labovitz, D.L.,et al, 2017
Reliability of Real-Time Video Smartphone for Assessing National Institutes of Health Stroke Scale Scores in Acute Stroke Patients
Stroke 43:3271-3277, Demaerschalk, B.,et al, 2012
A Review of the Evidence for the Use of Telemedicine Within Stroke Systems of Care: A Scientific Statement from the American Heart Association/American Stroke Association
Stroke 40:2616-2634, Schwamm,L.H.,et al, 2009
Recommendations for the Implementation of Telemedicine Within Stroke Systems of Care: A Policy Statement From the American Heart Association
Stroke 40:2635-2660, Schwamm,L.H.,et al, 2009
The Stroke-Thrombolytic Predictive Instrument: A Predictive Instrument for Intravenous Thrombolysis in Acute Ischemic Stroke
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