Large language models (LLMs) holds significant promise in achieving general medication recommendation systems owing to their comprehensive interpretation of clinical notes and flexibility to medication encoding. We evaluated both general-purpose and medical-specific LLMs for medication recommendations, showing their unsatisfactory precision and severe overprescription. To address this, we introduce Language-Assisted Medication Recommendation, which tailors LLMs for medication recommendation in a medication-aware manner, improving the usage of clinical notes. Fine-tuning LLMs with this framework can outperform existing methods by more than 10% in internal validation and generalize across temporal and external validations. Furthermore, the model maintains high accuracy when encountering out-of-distribution medication.
@article{arxiv.2503.03687,
title = {Fine-grained Alignment of Large Language Models for General Medication Recommendation without Overprescription},
author = {Zihao Zhao and Chenxiao Fan and Junlong Liu and Zheng Wang and Xiangnan He and Chongming Gao and Juan Li and Fuli Feng},
journal= {arXiv preprint arXiv:2503.03687},
year = {2025}
}