BrainTumorLLM:面向脑肿瘤诊疗的大语言模型优化与评估OA

To address the challenges faced by general-purpose medical Large Language Model(LLM)in the field of brain tumor care-namely the scarcity of domain-specific data,limited clinical adaptability,and insufficient accuracy of generated content.This paper proposes BrainTumorLLM,a specialized LLM tailored for brain tumor diagnosis and treatment.Built upon the Meta-LLaMA-3-8B-Instruct foundation model,BrainTumorLLM is optimized via Supervised Fine-Tuning(SFT)and Reinforcement Learning with Human Feedback(RLHF)and trained using a self-constructed,high-quality dataset named BrainTumorQA.This dataset comprises 11 000 question-answer pairs,encompassing both macro-level medical knowledge(symptoms,diagnostic methods,and treatment strategies)and micro-level clinical cases,with privacy safeguarded via anonymization and information constraint strategies.From a technical perspective,Low-Rank Adaptation(LoRA)is employed to enhance the training efficiency.A two-tier prompting framework is designed to guide the model in generating domain-specific responses at both the macro and micro levels.Furthermore,RLHF is integrated using an expert preference-driven optimization mechanism and a Proximal Policy Optimization(PPO)algorithm,reinforcing the clinical consistency of the generated content.The experimental results demonstrate that BrainTumorLLM significantly outperforms both general-purpose and medical-domain models in brain tumor-related question-answering tasks.In automatic evaluations,it achieves BLEU-1 and BLEU-2 scores of 0.338 3 and 0.268 4,respectively,and ROUGE-1,ROUGE-2,and ROUGE-L scores of 0.323 7,0.146 6,and 0.261 1,respectively.Moreover,the perplexity of the model is substantially reduced from 20.362(base model)to 7.674,highlighting its domain-specific precision,professional accuracy,and potential for clinical applications.BrainTumorLLM is a robust AI-powered tool that supports brain tumor diagnosis,treatment planning,and medical research.