Fine-Tuning DialoGPT on Common Diseases in Rural Nepal for Medical Conversations
This addresses healthcare delivery in resource-constrained rural settings by enabling offline medical conversations, though it is incremental as it applies an existing method to a new domain.
The study tackled the problem of providing conversational AI for healthcare in rural Nepal by fine-tuning DialoGPT on a synthetic dataset of doctor-patient interactions for ten common diseases, resulting in a model that produced coherent, contextually relevant, and medically appropriate responses offline.
Conversational agents are increasingly being explored to support healthcare delivery, particularly in resource-constrained settings such as rural Nepal. Large-scale conversational models typically rely on internet connectivity and cloud infrastructure, which may not be accessible in rural areas. In this study, we fine-tuned DialoGPT, a lightweight generative dialogue model that can operate offline, on a synthetically constructed dataset of doctor-patient interactions covering ten common diseases prevalent in rural Nepal, including common cold, seasonal fever, diarrhea, typhoid fever, gastritis, food poisoning, malaria, dengue fever, tuberculosis, and pneumonia. Despite being trained on a limited, domain-specific dataset, the fine-tuned model produced coherent, contextually relevant, and medically appropriate responses, demonstrating an understanding of symptoms, disease context, and empathetic communication. These results highlight the adaptability of compact, offline-capable dialogue models and the effectiveness of targeted datasets for domain adaptation in low-resource healthcare environments, offering promising directions for future rural medical conversational AI.