Aim/PurposeThe study aims to develop a multilingual healthcare assistance chatbot that provides real-time, accurate answers to a query related to health matters in multiple languages. Conversion of written responses into spoken words lets users have the medical information necessary for them without interrupting communication between patients and health services. The purpose of this system is to break the language barriers for healthcare users, making it easier for them to access vital medical advice and resources.

BackgroundThis research focuses on fine-tuned large language models (LLMs) for providing accurate, context-aware responses in multiple languages with speech-based output. The chatbot, built on pre-trained Hugging Face models and fine-tuned with healthcare datasets, demonstrates a comprehensive understanding of medical terminology, symptoms, and healthcare concepts across languages. Unlike many existing chatbots that offer limited medical knowledge or support only a single language, the proposed chatbot leverages fine-tuning on a specialized medical corpus to deliver more accurate, context-rich responses. Furthermore, it provides text-based and speech-based outputs, improving user engagement and accessibility compared to text-only models.

MethodologyA multilingual healthcare assistance chatbot is proposed using the pre-trained model aboonaji/llama2finetune-v2 and the specialized medical dataset aboonaji/wiki_medical_terms_llam2_format from Hugging Face. Key steps in the methodology include cleaning and normalizing medical terms, symptoms, and treatment advice to ensure uniformity across multiple languages. The model is fine-tuned on this healthcare dataset, enabling accurate and context-sensitive responses. Text-to-speech (TTS) technology is integrated to provide natural-sounding, voice-based answers, enhancing accessibility. Multilingual capability is ensured through modules for smooth language transitions. The chatbot is deployed on an intuitive web or mobile platform, simplifying user interaction. Performance metrics, including response accuracy, linguistic consistency, and user satisfaction, continuously improve through feedback and periodic updates with evolving medical knowledge and language models.

ContributionThis research adds value to the medical sector by maximizing access to healthcare information across heterogeneous linguistic groups. It uses advanced natural language processing techniques and text-to-speech technology, facilitating quick and efficient interaction between patients and health providers. This allows users to follow crucial medical advice and information in their preferred language, thus promoting greater patient understanding and engagement. The output of the accurate, context-sensitive responses to healthcare search terms given by the chatbot assists in bridging the gap between patients and medical resources to make informed decisions for better overall health literacy. This model works as an instrumental instrument in solving language barriers in healthcare by introducing inclusiveness and promoting a stronger case for equality in healthcare.

FindingsResults indicate that the chatbot effectively addresses language gaps in healthcare by generating contextually accurate and relevant responses to medical queries with excellent quality and reliability. Performance metrics demonstrate a BLEU score between 0.8 and 0.9, a perplexity score of 80.45, and an average latency of 20 seconds, highlighting robust translation accuracy, coherent response generation, and reasonable interaction time. Text-to-speech integration enhances accessibility and user engagement, while high user satisfaction confirms its potential to improve health literacy and patient comprehension. Continuous feedback during testing has enabled iterative refinements, ensuring the chatbot remains a reliable and inclusive tool for medical information delivery.

Recommendations for PractitionersClinical practitioners should also encourage the adoption of the multilingual healthcare assistance chatbot in their clinical settings to enhance engagement and communication with the patient. This will enable healthcare providers to effectively bridge the language gap to provide patients with the exact health information they wish to receive in the language. The practice should encourage the different patient populations to use the chatbot and assist them in seeking information confidently.

Recommendation for ResearchersIt would be great to challenge this multilingual medical assistance chatbot through further research, for example, testing in other languages and improving its natural language processing properties to provide users with accurate medical answers. This study should be followed by further studies measuring the extended effects of chatbots on health literacy and patient outcomes in different healthcare settings. Collaboration in design with healthcare professionals will provide insights into user needs and ensure the chatbot remains practical and meaningful. Additionally, artificial intelligence and machine learning could enhance the learning of the interactions from the interactions with the chatbot, thus enhancing its effectiveness over time. These efforts can significantly advance technology in healthcare communication and patient support.

Impact on SocietyA multilingual health assistance chatbot can greatly affect society by giving diverse populations easy access to essential health information. It bridges the language barrier gaps, enabling individuals of many linguistic backgrounds to gain self-confident medical advice and information, thus furthering health literacy and informed decision-making. That enables better healthcare because the patients will be able to understand what might be wrong with them and likely to comply with some prescriptions made for the treatment. In addition, the chatbot encourages egalitarian healthcare because it allows for the inclusion of marginalized groups of society to be treated equally. At the same time, there should still be an equal occurrence in the healthcare system. This means, in turn, that the chatbot does not only enhance individual results in health but also community health at large because it is sure to encourage proactive engagement with the services and healthcare resources.

Future ResearchFuture research may focus on expanding the knowledge database for the chatbot by incorporating many languages and dialects. It could also work on perfecting the natural language processing to interpret complex medical-related queries better. The integration of more advanced techniques of artificial intelligence may also further enhance the learning abilities of chatbots from user interactions and sharpen their response across time cycles.