Current Situation
Owing to advancements in generative AI, machine learning, natural language processing, and deep learning algorithms, AI is increasingly applied to language translation in healthcare.
Goals and Objectives
It has the ability to facilitate real-time communication between patients and healthcare professionals who may not share a common language.
Improve patient care, outcomes, and efficiency within healthcare settings.
Can be used to translate medical documents and research from various languages, aiding global collaboration in the health sciences.
Technology Deployed
EHRs
Generative pretrained transformers and large language models
Machine learning
Deep learning
Natural language processing
Neural machine translation
Speech recognition
Use Case Summary
In a multilingual urban healthcare setting (e.g., a large hospital), communication barriers can exist due to diverse patient backgrounds. AI-powered clinical translation facilitates real-time, accurate translation between patients and healthcare professionals. This ensures improved communication, high-quality care, and better health outcomes while maintaining robust data privacy measures.