The rapid development of large language models (LLMs) has transformed many industries, including healthcare. However, previous medical LLMs have largely focused on leveraging general medical knowledge to provide responses, without accounting for patient variability and lacking true personalization at the individual level. To address this, we propose a novel method called personalized medical language model (PMLM), which explores and optimizes personalized LLMs through recommendation systems and reinforcement learning (RL). Specifically, by utilizing self-informed and peer-informed personalization, PMLM captures changes in behaviors and preferences to design initial personalized prompts tailored to individual needs. We further refine these initial personalized prompts through RL, ultimately enhancing the precision of LLM guidance. Notably, the personalized prompt are hard prompt, which grants PMLM high adaptability and reusability, allowing it to directly leverage high-quality proprietary LLMs. We evaluate PMLM using real-world obstetrics and gynecology data, and the experimental results demonstrate that PMLM achieves personalized responses, and it provides more refined and individualized services, offering a potential way for personalized medical LLMs.

翻译：大型语言模型（LLM）的快速发展已变革了包括医疗在内的众多行业。然而，先前的医疗LLM主要侧重于利用通用医学知识提供应答，未能考虑患者差异性，且缺乏个体层面的真正个性化。为解决这一问题，我们提出了一种称为个性化医疗语言模型（PMLM）的新方法，该方法通过推荐系统和强化学习（RL）探索并优化个性化LLM。具体而言，通过利用自感知与同伴感知的个性化机制，PMLM捕获行为与偏好的变化，从而设计出贴合个体需求的初始个性化提示。我们进一步通过RL对这些初始个性化提示进行优化，最终提升LLM指导的精确度。值得注意的是，个性化提示采用硬提示形式，这使得PMLM具备高度的适应性和可复用性，能够直接利用高质量的专有LLM。我们使用真实世界的妇产科数据对PMLM进行评估，实验结果表明PMLM实现了个性化应答，并提供更精细化和个体化的服务，为个性化医疗LLM的发展提供了一条潜在路径。