AI-Enhanced Language Translation for Healthcare: A Review of Applications

Abstract

Language barriers pose significant challenges in healthcare, affecting effective communication between patients and healthcare providers, hindering access to medical information, and impeding global collaboration in medical research. This paper presents a comprehensive review of the applications and implications of AI-enhanced language translation in healthcare. We delve into the evolution of language translation technologies, emphasizing the transformative role of artificial intelligence, particularly Neural Machine Translation (NMT). The review explores the benefits of AI-enhanced translation, including improved accessibility to healthcare services, enhanced patient-doctor communication, and accelerated knowledge dissemination. However, challenges such as ensuring accuracy, addressing security and privacy concerns, and navigating ethical implications are discussed in detail. Examining real-world applications, the paper presents case studies illustrating successful implementations in telehealth, multilingual electronic health records (EHR), and healthcare education. These case studies highlight both the achievements and challenges faced in diverse healthcare settings. Future trends and developments, including advancements in neural translation models and customization for the healthcare domain, are explored, providing insights into the evolving landscape of language translation technologies. Implications for healthcare accessibility and recommendations for responsible implementation are discussed. This review underscores the transformative potential of AI-enhanced language translation in healthcare, offering a roadmap for researchers, healthcare practitioners, and policymakers to navigate the opportunities and challenges in harnessing this technology for the betterment of global healthcare delivery.