Using large language models for therapeutic drug monitoring reporting – a proof-of-concept

Abstract Objectives Therapeutic drug monitoring (TDM) reports are critical for precision medicine but remain labor-intensive. This study attempts a proof-of-concept approach to investigate the possibilities of large language models (LLMs) in generating TDM reports. Methods Using fictional cases based on real cases and a questionnaire-based framework, we evaluated completeness, lack of false information, evidence, appropriateness and relevance of the generated reports using the CLEAR framework. Results While basic constellations were acknowledged by the models and the TDM reports followed the structure of the prompt, challenges remain in context understanding and domain-specific nuance. Importantly, we examined only standalone LLM outputs rather than human-AI collaboration. Conclusions Our findings suggest LLM-assisted TDM reporting can reduce clinical workload and enhance standardization. Further research should focus on improving model interpretability, refining data integration, and designing systems that effectively incorporate human oversight.