Knowledgeable Language Models as Black-Box Optimizers for Personalized Medicine

Area of Science:

• Biomedical informatics
• Artificial intelligence in medicine
• Computational biology

Background:

• Personalized medicine seeks to tailor treatments to individual patients based on genetic and environmental factors.
• Current surrogate models for treatment efficacy assessment often fail to generalize to new patient-treatment combinations.
• Domain-specific prior knowledge, like medical texts and knowledge graphs, can offer valuable insights into treatment effectiveness.

Purpose of the Study:

• To introduce a novel approach for leveraging large language models (LLMs) as black-box optimizers for personalized medicine.
• To develop a method that integrates domain-specific prior knowledge into treatment optimization without task-specific fine-tuning.
• To propose personalized treatment plans in natural language using LLM capabilities.

Main Methods:

• LLM-based Entropy-guided Optimization with Knowledgeable priors (LEON) was developed as a mathematically principled approach.
• LEON utilizes LLMs as stochastic engines for proposing treatment designs via 'optimization by prompting'.
• The method leverages LLMs' ability to contextualize unstructured domain knowledge for treatment optimization.

Main Results:

• LEON demonstrated superior performance in proposing individualized treatments compared to traditional methods.
• Experiments showed LEON outperformed existing LLM-based approaches in real-world optimization tasks.
• The approach effectively uses LLMs to generate personalized treatment plans by integrating prior medical knowledge.

Conclusions:

• LEON offers a robust and effective framework for personalized medicine by integrating LLMs and prior knowledge.
• The method advances the application of LLMs in healthcare by enabling black-box optimization without fine-tuning.
• LEON shows significant potential for improving the discovery of optimal, individualized treatment regimens.