Rule-Augmented Artificial Intelligence-empowered Systems for Medical Diagnosis using Large Language Models
Dimitrios P. Panagoulias, Filippos A. Palamidas, Maria Virvou, George A. Tsihrintzis
Abstract
In this paper, we investigate the enhancement of Artificial Intelligence (AI) technologies in healthcare and the better understanding of medical literature with the use of Large Language Models (LLMs) and Natural Language Processing (NLP). Specifically, we introduce a rule-augmented AI-empowered system which incorporates a rule-based decision system, the ChatGPT application programming interface (API), and other external machine learning and analytical APIs to offer diagnostic suggestions to patients. The complexities of patient healthcare experiences, including doctor-patient interactions, understanding levels, treatment procedures, and preventive care, are considered. We illustrate how a diagnostic process typically integrates various strategies depending on various factors. To digitize the greatest portion of the process, we propose and illustrate the use of LLMs for humanizing the communication process and investigating ways to reduce burdens and costs in primary healthcare. We also outline a theoretical decision model for evaluating the use of technological components from external sources versus building them from scratch. The paper is structured into sections detailing background theories and context, our proposed and implemented rule-augmented AI-empowered system, as well as a system test in a corresponding use case. Finally, the paper key findings are presented, which contribute valuable insights for future work in this field.