Exploring Cross-Linguistic Transfer Learning for Real-Time Diagnosis Support (KEY IJP************114)

• Priyanshi

Abstract

In a multilingual world, language barriers often stand in the way of equitable access to healthcare. This paper delves into the transformative power of cross-linguistic transfer learning in boosting the diagnosis of global health through the integration of real-time diagnosis systems with multilingual natural language processing models. By leveraging advanced transfer learning techniques, such as multilingual fine-tuning, zero-shot learning, and cross-lingual embeddings, this study aims to bridge gaps in healthcare delivery, especially in under-resourced regions where medical data is scarce or exists in local languages.The research investigates how multilingual NLP models can be trained to process medical texts, clinical notes, and patient records across multiple languages, providing accurate diagnostic insights and actionable recommendations in real time. It emphasizes pre-trained language models like mBERT, XLM-R, and BLOOM, adapting it to medical context through domain-specific datasets. Other challenges have been addressed within the framework of implementing these systems - data scarcity in low-resource languages, algorithmic biases, and lack of interpretability in AI systems.The paper proposes innovative solutions to overcome these challenges, including synthetic multilingual data generation and collaborative data annotation using explainable AI (XAI) frameworks to ensure trust and transparency in diagnostics. Case studies will be presented to show practical applicability of cross-linguistic NLP models for purposes such as enhancing multilingual support in chatbots for patients, automating disease surveillance from health reports in various languages, and enabling diagnostic tools to work equally well across diverse linguistic settings.This work highlights the prospect in democratizing health access with cross-linguistic NLP systems, mitigating further disparities in health across boundaries and creating a diagnosis setting where inclusiveness would shine through. It makes access to quality health care ubiquitous in terms of both geology and linguistics across cultures.