Large Language Model (LLM) Support for Preliminary Consultation in Healthcare

The growing demand for accessible, efficient, and timely healthcare services underscores the need for innovative solutions, particularly in preliminary medical consultations. This research explores the potential of Large Language Models (LLMs) to support the diagnosis and symptom assessment process, providing a scalable and effective solution to improve accessibility to healthcare. Leveraging domain-specific data, this study develops a fine-tuned LLM designed to improve both the precision and usability of healthcare interactions. The focus is on the development and implementation of a conversational platform that allows users to accurately describe symptoms, receive preliminary diagnoses, and obtain actionable medical advice. By integrating clinical knowledge with advanced natural language processing, the platform assists individuals in identifying potential health issues early, thereby promoting early intervention and preventive care. The outcomes of this research aim to bridge critical gaps in healthcare accessibility, particularly for underserved populations, by empowering users with valuable medical knowledge and reducing reliance on immediate professional consultations.