Refining medical large language models: key insights from instruction tuning

Abstract

This literature review introduces a comprehensive summary of the most recent scholarly work on instruction-tuning strategies for medical large language models (Med-LLMs). It begins by reviewing three fundamental approaches to creating an instruction dataset: human-crafted datasets, synthesized datasets generated by LLMs, and datasets that incorporate Retrieval-Augmented Generation (RAG). This article explores the role of medical instruction datasets by reviewing thirteen different medical models, evaluating their effectiveness across multiple clinical tasks, and examining how their utilization can improve outcomes in the medical domain. This research discusses key insights for optimizing instruction-based fine-tuning of language models. It analyzes the effectiveness of the phased instruction method and the benefits of integrating mixed-prompt techniques. Additionally, it assesses the effect of choosing an appropriate backbone model before fine-tuning. Furthermore, it demonstrates how the selection of words when crafting instructions influences a model’s performance. The survey emphasizes that carefully curated instructional data, coupled with well-crafted strategies, can greatly enhance the potential of Med-LLMs in real-world healthcare applications. Nevertheless, several challenges must be addressed to ensure the safe, ethical, and effective deployment of Med-LLMs. This article outlines future research directions, including mitigating racial and gender biases, leveraging external knowledge sources, and reinforcing privacy through robust anonymization of patient information and regulatory adherence ( e.g ., Health Insurance Portability and Accountability Act (HIPAA)). Addressing these challenges will pave the way for reliable, safe, and ethical artificial intelligence (AI)-driven healthcare applications.

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