Jul 2026· Nature Journal of Emerging Sciences Technologies and Innovations· Vol 9, pp. 447-465· 0 citations
TL;DR
Comparative analysis confirmed that the proposed bilingual NLP model outperforms existing monolingual and rule-based systems in linguistic inclusiveness and accessibility.
Abstract
The research came up with a bilingual natural language processing (NLP) model of a patient-focused healthcare system that is geared towards improving healthcare communication in low-resource settings, based on both the English and Yoruba languages. The system combines multilingual transformer-based text processing, speech-to-text and text-to-speech modules, retrieval-augmented response generation, and offline-compatible deployment to provide culturally adaptive and personalized health information. Assessment was made based on conventional performance measures such as accuracy, cultural relevance, readability, and response quality through pilot testing and comparison against other existing bilingual models of healthcare delivery. The experimental findings indicated a general classification accuracy of 91%, which revealed a high language interpretation and response-generating ability. The system also achieved cultural relevance of 40% by adapting to indigenous healthcare communication requirements and readability of 91% by simplifying complex medical information into patient-friendly explanations. Analysis indicated that linguistic inclusiveness and accessibility were enhanced as compared to the current monolingual practices. The results confirm that bilingual NLP models have the potential to significantly reduce communication barriers, enhance patient comprehension, and promote equitable healthcare practices in bilingual and underserved communities. Comparative analysis confirmed that the proposed bilingual NLP model outperforms existing monolingual and rule-based systems in linguistic inclusiveness and accessibility. The results validate that bilingual NLP models can significantly reduce communication barriers, enhance patient comprehension, and promote equitable healthcare delivery in multilingual, underserved communities.
Hospitals frequently face challenges in delivering timely and accessible information to patients due to high inquiry volumes, language barriers, and limited staff availability. This paper proposes a multilingual, voice-enabled hospital chatbot that provides real-time assistance through both text and speech interfaces. The proposed system leverages Large Language Model (LLM)-based sentence embeddings using Sentence-BERT for semantic similarity-driven question answering, along with Google Translate API for multilingual support and Google Text-toSpeech for voice responses. The chatbot supports multiple Indian languages, including English, Hindi, Telugu, Tamil, Kannada, and Marathi, enabling inclusive communication across diverse user groups. Designed as a Flask-based web application with a responsive Bootstrap interface, the system aims to achieve effective contextual understanding, reduced response time, and improved accessibility when compared to traditional rule-based hospital inquiry systems. The proposed approach highlights the potential of LLM-driven semantic retrieval-based conversational agents in enhancing patient engagement and improving operational efficiency in healthcare environments.
Kamisetty Mythri Sridevi, Kallagunta Srividhya· International Journal of Eng...· 0 citations
This study evaluates six AI medical translation systems using a mixed-methods approach, integrating BLEU scores, user surveys (N=775), and behavioral data. A standardized bilingual corpus was constructed from authoritative sources including the WHO and NMPA, while an AHP-BLEU hybrid model was developed to combine subjective user evaluations with objective scores across word, sentence, and paragraph levels in both Chinese-English and English-Chinese tasks. Results show Atman and Youdao outperform others in overall quality, with DeepL excelling in terminology. Spearman correlation analysis confirms a strong positive association (ρ=0.943, p=0.005) between BLEU scores and user satisfaction, validating the model. Despite rapid advances, current AI medical translation tools still struggle with term accuracy, context adaptation, and document complexity. The proposed AHP-BLEU framework helps align evaluation with user priorities, offering a more balanced view of performance. Future improvements should include semantic-aware metrics and human-verified baselines to better support multilingual medicine applications, from Traditional Chinese Medicine globalization to virtual consultations.
Xiangyu Wu, Yongqi Zeng, Qing Wang et al.· Advances in Engineering Tech...· 0 citations
This work introduces IndicMedQA, a novel multimodal AI framework that integrates Indic large language models (LLMs) and visual encoders to analyze patient inquiries using both textual and visual cues, and creates a multilingual multimodal medical corpus spanning seven major Indian languages, translated using a semi-automated approach.
Akash Ghosh, Arkadeep Acharya, M. Muhsin et al.· ACM Transactions on Computin...· 1 citation
The hybrid pipeline integrating rule-based dictionary normalization with large language model (LLM)–based postprocessing significantly improved Korean-English code-switched medical ASR accuracy.
Chanryeong Oh, Yul Hwangbo, Wonjoong Cheon et al.· Journal of Medical Internet...· 0 citations
A novel benchmark comprising over 9,000 real-world, point-of-care, multilingual, and multimodal clinical question-answer pairs sourced from frontline health workers in Nigeria reveals several critical insights into the suitability of LLMs as clinical decision support systems in low-resource contexts.
Tobi Olatunji, C. Aka, C. Okocha et al.· medRxiv· 0 citations
Medical text simplification is important for improving health literacy and making clinical information easier for patients to understand. While clinical text simplification has been widely studied in English, Spanish remains underexplored, especially for systematic adaptation into plain-language in clinical settings. This work presents the first benchmark of small language models for Spanish clinical plain-language adaptation and introduces MEDICLARO, a corpus specifically designed for this task. MEDICLARO consists of 50 clinical notes, each with three human-written simplifications produced by cognitive accessibility experts in accordance with ISO 24495-1:2023. Four families of state-of-the-art language models were evaluated through fine-tuning and prompt-based strategies. The evaluation covers simplification, semantic similarity, factual consistency, readability, and environmental impact, and is complemented by human evaluation and qualitative error analysis. The results show that Llama-3.2-3B provides the most balance profile across efficiency, robustness, and overall performance, while RigoChat-v2-7B stands out when output quality and readability are prioritized. Overall, this work establishes a solid foundation for integrating small language models into Spanish clinical workflows, offering a sustainable, patient-centered path toward digital health accessibility.
P. Martínez, Jesús M. Sánchez-Gómez, Lourdes Moreno· Scientific Reports· 0 citations