A General-purpose AI Avatar in Healthcare

TL;DR

This paper presents a framework for developing general-purpose AI avatars in healthcare, utilizing prompt engineering and fine-tuning of large language models to enhance patient interaction and engagement.

Contribution

It introduces a novel two-phase approach for creating customizable medical AI avatars with improved conversational abilities and personality traits.

Findings

Framework demonstrated with a three-category prompt dictionary

Prompt engineering improves chatbot personality and engagement

Future work includes enhancing context understanding and output accuracy

Abstract

Recent advancements in machine learning and natural language processing have led to the rapid development of artificial intelligence (AI) as a valuable tool in the healthcare industry. Using large language models (LLMs) as conversational agents or chatbots has the potential to assist doctors in diagnosing patients, detecting early symptoms of diseases, and providing health advice to patients. This paper focuses on the role of chatbots in healthcare and explores the use of avatars to make AI interactions more appealing to patients. A framework of a general-purpose AI avatar application is demonstrated by using a three-category prompt dictionary and prompt improvement mechanism. A two-phase approach is suggested to fine-tune a general-purpose AI language model and create different AI avatars to discuss medical issues with users. Prompt engineering enhances the chatbot's conversational abilities and personality traits, fostering a more human-like interaction with patients. Ultimately, the injection of personality into the chatbot could potentially increase patient engagement. Future directions for research include investigating ways to improve chatbots' understanding of context and ensuring the accuracy of their outputs through fine-tuning with specialized medical data sets.