Preliminary Results from Using Gen-AI to Personalized Medication Leaflets

Abstract

This paper explores potential of Large Language Models (LLMs) to generate concise personalized summaries of PILs, guided by data from IPS, as an approach to enhance patient understanding of and adherence to medication. Providing digestible personalized summaries of PILs in simplified language can foster shared decision making and enhance patient communication with their health team. LLMs can generate coherent summaries, however, accuracy and personalization need to improve. Future research directions in personalization of PILs can include prompt engineering, fine-tuning, and evaluation models for testing at scale to understand potentials of tailored PILs for engagement in patient-physician interaction and shared decision-making.

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