Effective Doctor-Patient Communication (DPC) is crucial for enhancing healthcare outcomes, with Personalized Health Education (PHE) playing a significant role. Recent studies have indicated Large Language Models (LLMs) hold potential in the healthcare. However, the applications of LLMs face high resource demands, cost, and security concerns. To address these issues, lightweight LLMs have emerged. This study developed a system with lightweight LLM to improve DPC by generating PHE reports based on individual patient information. Initially, a disease classification model was developed based on medical images and converted into textual input for the lightweight LLM as multimodal input. Then, employing knowledge distillation (KD) technology to extract PHE knowledge from the LLM and fine-tune lightweight LLM to PHE model. The first experiment indicated that the disease classification model boasts a high level of accuracy, serving as the foundation for the multimodal input the system. In the second experiment, doctors reviewed and evaluated PHE reports generated by the PHE model, with average scores exceeding 4 points, similar with the LLMs and significantly different from the pre-finetuned model (p<0.05). This study also evaluated the performance of different model sizes and datasets in fine-tuning PHE model. The third experiment implemented Retrieval-Augmented Generation (RAG), enabling the system to integrate the latest hospital information, such as doctors' consultation hours, demonstrating its advantage in generating customized content. The fourth experiment incorporated Reinforcement Learning with Human Feedback (RLHF), retraining PHE model based on medical staff modifications to PHE reports, making the generated reports more aligned with user needs. Overall, PHE model developed using KD techniques successfully reduced the model size and resource requirements while maintaining the information accuracy and effectiveness, enhancing its potential for clinical application. Through RAG and RLHF, the system achieved customization and continuous training in the private III environment, protecting the intellectual property of medical staff and patents’ privacy. This system generated PHE reports based on patient information, serving as a reference for PHE to reduce the burden on medical staff and offering patients tailored reports, improving DPC and healthcare outcomes. This study demonstrated the potential of this system in clinical settings and future related applications.