Tutorial: guidance on the use of large language models for medical research

Abstract

Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4 and DeepSeek-R1, represent a transformative class of artificial intelligence tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Their potential application spans a broad range of medical tasks, such as clinical documentation, matching patients to clinical trials and answering medical questions. Here in this Tutorial, we discuss an actionable set of best practices to help healthcare professionals utilize LLMs more effectively and efficiently. The overall workflow follows sequential phases from formulating the task, choosing the most appropriate LLMs, engineering the prompts, fine-tuning the requests and through to model deployment. We discuss a set of critical considerations in identifying medical tasks that align with the core capabilities of LLMs and selecting models based on the required task, data, performance and model interface. We then review the strategies, such as prompt engineering and fine-tuning, to adapt standard LLMs to specialized medical tasks. We then cover deployment considerations, including regulatory compliance, ethical guidelines and continuous monitoring for fairness and bias. By providing a structured step-by-step methodology, this entry-level tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner.