Outlining the Borders for LLM Applications in Patient Education: Developing an Expert-in-the-Loop LLM-Powered Chatbot for Prostate Cancer Patient Education
<|reference_start|>Outlining the Borders for LLM Applications in Patient Education: Developing an Expert-in-the-Loop LLM-Powered Chatbot for Prostate Cancer Patient Education: Cancer patients often struggle to transition swiftly to treatment due to limited institutional resources, lack of sophisticated professional guidance, and low health literacy. The emergence of Large Language Models (LLMs) offers new opportunities for such patients to access the wealth of existing patient education materials. The current paper presents the development process for an LLM-based chatbot focused on prostate cancer education, including needs assessment, co-design, and usability studies. The resulting application, MedEduChat, integrates with patients' electronic health record data and features a closed-domain, semi-structured, patient-centered approach to address real-world needs. This paper contributes to the growing field of patient-LLM interaction by demonstrating the potential of LLM-based chatbots to enhance prostate cancer patient education and by offering co-design guidelines for future LLM-based healthcare downstream applications.<|reference_end|>
@article{hao2024outlining,
    title={Outlining the Borders for LLM Applications in Patient Education:
  Developing an Expert-in-the-Loop LLM-Powered Chatbot for Prostate Cancer
  Patient Education},
    author={Yuexing Hao, Jason Holmes, Mark Waddle, Nathan Yu, Kirstin Vickers,
  Heather Preston, Drew Margolin, Corinna E. L"ockenhoff, Aditya Vashistha,
  Marzyeh Ghassemi, Saleh Kalantari, Wei Liu},
    journal={arXiv preprint arXiv:2409.19100},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19100},
    primaryClass={cs.HC}
}