RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions
<|reference_start|>RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions: Clinical question answering systems have the potential to provide clinicians with relevant and timely answers to their questions. Nonetheless, despite the advances that have been made, adoption of these systems in clinical settings has been slow. One issue is a lack of question-answering datasets which reflect the real-world needs of health professionals. In this work, we present RealMedQA, a dataset of realistic clinical questions generated by humans and an LLM. We describe the process for generating and verifying the QA pairs and assess several QA models on BioASQ and RealMedQA to assess the relative difficulty of matching answers to questions. We show that the LLM is more cost-efficient for generating "ideal" QA pairs. Additionally, we achieve a lower lexical similarity between questions and answers than BioASQ which provides an additional challenge to the top two QA models, as per the results. We release our code and our dataset publicly to encourage further research.<|reference_end|>
@article{kell2024realmedqa:,
    title={RealMedQA: A pilot biomedical question answering dataset containing
  realistic clinical questions},
    author={Gregory Kell, Angus Roberts, Serge Umansky, Yuti Khare, Najma Ahmed,
  Nikhil Patel, Chloe Simela, Jack Coumbe, Julian Rozario, Ryan-Rhys Griffiths,
  Iain J. Marshall},
    journal={arXiv preprint arXiv:2408.08624},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08624},
    primaryClass={cs.CL cs.AI}
}