Enhancing Medical Diagnosis with Fine-Tuned LLMs

Abstract

The development of tools such as OpenAI's ChatGPT and Anthropic's Claude have greatly advanced humancomputer interaction. This is due to the advancements made in the Natural Language Processing (NLP) field by Large Language Models (LLMs). The pre-training and instruction fine-tuning undergoes makes these models capable of generating coherent and contextually relevant responses. Unfortunately, posing real world problems lacking critical reasoning or understanding renders these models ineffective in the medical domain due to factual inaccuracies. To solve this problem, enhancing precision and relevance to specific tasks necessitates fine-tuning LLMs to more focused datasets. In this present study, we introduce MedBot, a chatbot for medical diagnosis using self-reported symptoms. MedBots training base included many medical textbooks that incorporate crucial clinical laboratory knowledge reasoning, and contemporary artificial intelligence, aligned with cognitive science. These combined features, not only provide trustworthy and clear reasoning but allow for far more accurate and timely understanding.

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