Self-Evolving LLM Ecosystems for Precision Medicine
Keywords:
Precision Medicine, Self-Evolving LLM, Treatment Optimization, Reinforcement Learning, Personalized Medication, Clinical AI, Multi-Agent Systems, Random Forest ClassifierAbstract
The emergence of Large Language Models (LLMs) has revolutionized clinical decision-making, yet most remain static post-deployment. This research introduces a self-evolving LLM ecosystem designed for precision medicine, capable of adapting continuously to real-time clinical data, genomic profiles, and treatment outcomes. Based on a structured dataset of personal medications integrated with patient demographics, diagnoses, treatments, and outcomes, this paper emulates a shifting learning mechanism as a result of reinforcement-based retraining and the returns of LLM-agents via feedback loops. An evolution of a Random Forest based TreatmentAgent is performed and the performance is measured over five evolution cycles. The predictive accuracy of the model increases by 14% to 41% based on fine-tuning through heavier data samples. An LLM-agent simulator with rules is proposed to recommend treatment refinements using side effects and time of recovery. Exploratory data analysis reveals valuable patterns such as diagnosis-related length of recovery and BMI differentiation to three levels of treatment effectiveness. This study produces an experimental blueprint of how changing AI agents can power hyper-personalized drug choice. The results indicate the viability as well as revolutionary of installing self-evolving intelligence in healthcare infrastructures to maximize patient-specific treatment regimens at scale.
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