@article{jin2026tutorial,
  title={Tutorial: guidance on the use of large language models for medical research},
  author={Jin, Qiao and Wan, Nicholas and Leaman, Robert and Tian, Shubo and Wang, Zhizheng and Yang, Yifan and Wang, Zifeng and Xiong, Guangzhi and Lai, Po-Ting and Zhu, Qingqing and Hou, Benjamin and Sarfo-Gyamfi, Maame and Zhang, Gongbo and Gilson, Aidan and Bhasuran, Balu and He, Zhe and Zhang, Aidong and Sun, Jimeng and Weng, Chunhua and Summers, Ronald M. and Chen, Qingyu and Peng, Yifan and Lu, Zhiyong},
  journal={Nature Protocols},
  pages={1--12},
  year={2026},
  publisher={Nature Publishing Group UK London},
  doi = {10.1038/s41596-026-01408-z},
  url = {https://doi.org/10.1038/s41596-026-01408-z},
  code = {https://github.com/ncbi-nlp/LLM-Medicine-Primer},
  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 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 strategies such as prompt engineering and fine-tuning to adapt standard LLMs to specialized medical tasks, followed by deployment considerations including regulatory compliance, ethical guidelines and continuous monitoring for fairness and bias.},
}