Exploring the Potential of Large Language Models to Create Explainable Medical Recommendations

Abstract

The development of digital technologies and artificial intelligence is contributing to the evolution of decision support systems in medicine, where there is a growing interest in health recommendation systems (HRSs), which today requires an expansion of the knowledge base concerning the surrounding world to improve the explainability of the recommendations provided. In the paper, the potential of three ChatGPT models (GPT-3.5, GPT-4, and GPT-4o) for generating explanations of cardiovascular risk factors has been evaluated. The results of the analysis show that these models have significant potential to generate understandable, safe, and correct explanations of disease risk factors, which can form the basis for the development of promising HRSs to automate the preparation of medical reports and providing patients with explainable recommendations.

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