Project Team
Students
Aman Sahu
Computer Science
Penn State Harrisburg
Andrew Hoang
Computer Science
Harrisburg
Faculty Mentors
Hien Nguyen
Harrisburg
Computer Science
Project
Project Video
Project Abstract
Language barriers within healthcare settings can pose a challenge for non-English speaking patients. This language barrier often leads to miscommunication between the doctor and the patient. To encounter this problem, this project aims to develop a mobile application in SwiftUI that utilizes Meta’s SeamlessM4T-v2 AI Model to provide real-time conversational translations between doctors and patients. By combining SeamlessM4T-v2 with a headset, the app can deliver accurate and context-specific translations of conversations doctors and patients may have. The development of this application involved integrating and deploying the SeamlessM4T-v2 model onto Amazon’s SageMaker, creating a user-friendly interface using SwiftUI, and creating an API endpoint using Python libraries like flask and transformers. This paper explores the methodologies used for building and implementing this mobile translation app which aims to improve healthcare diagnosis for patients with Limited English Proficiency.
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