To meet this diagnostic challenge, John J. Volpi, MD, Director, Eddy Scurlock Stroke Center and Associate Professor of Clinical Neurology, and his team developed an effective and rapid stroke diagnostic tool. At the 2021 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, the Houston Methodist collaborative group of Volpi and Stephen T.C. Wong, John S. Dunn Presidential Distinguished Chair in Biomedical Engineering and Professor of Computer Science & Bioengineering in Oncology, presented their work on a novel smartphone app developed in collaboration with researchers at Pennsylvania State University. The app uses a machine learning algorithm for computer-aided evaluation of facial movement weaknesses and speech in patients to determine the presence of stroke; it emulates the ED triage process using a smartphone. The app is an intelligent-augmented lifelike avatar mobile app (iLAMA) designed to automate and streamline the NIH Stroke Scale physical examination by integrating computer vision and sensor readings. Designed for elderly patients, the app features an animated 3D model of a physician who guides the user through the exam consisting of five core tasks. The first two involve rolling the eyes to the left and then right, and then smiling as wide as possible. The app identifies facial landmarks and analyzes facial palsy. In the next task, the user extends the arm, holding the phone at shoulder level so the phone’s gyroscope can detect acceleration to determine possible arm weakness. Then the app tracks location of the hand key points and looks for ataxia-based accuracy of the touch locations. Finally, using an accelerometer, the app calculates the user's forward acceleration while walking and detects possible imbalances. The analyzed results of these tasks are then sent to a specified neurologist or stroke specialist for review. To train the AI model, the researchers created a dataset from more than 250 Houston Methodist patients experiencing stroke symptoms. Using an iPhone to record the encounter, each patient performed a test to demonstrate their speech and communication. The results of the study were highly significant. Their app was 93% correct in detecting a possible stroke compared to the ED physician rate of 71%, and the app was also better at avoiding overdiagnosis with an accuracy of 79% compared with the physician’s rate of 73%. Of note, the app encounter took less than five minutes and did not include any additional data, such as scan results or the physician’s prior history. This rapid stroke assessment app will allow expedited diagnosis of stroke by physicians, and faster delivery of the most effective treatment options. Future versions of the app are already under development to give those at higher risk for stroke, and the ability to perform a self-assessment immediately upon symptom presentation by their caregivers. Ultimately, this would further reduce the time from stroke onset to effective treatment and significantly improve patient outcomes, increasing the chances of survival and recovery. By combining an AI tool with the emergency room physician’s clinical knowledge, this “augmented intelligence” innovation will be a key advantage to preventing and/or surviving a stroke.