Learnings from a Large-Scale Deployment of an LLM-Powered Expert-in-the-Loop Healthcare Chatbot
<|reference_start|>Learnings from a Large-Scale Deployment of an LLM-Powered Expert-in-the-Loop Healthcare Chatbot: Large Language Models (LLMs) are widely used in healthcare, but limitations like hallucinations, incomplete information, and bias hinder their reliability. To address these, researchers released the Build Your Own expert Bot (BYOeB) platform, enabling developers to create LLM-powered chatbots with integrated expert verification. CataractBot, its first implementation, provides expert-verified responses to cataract surgery questions. A pilot evaluation showed its potential; however the study had a small sample size and was primarily qualitative. In this work, we conducted a large-scale 24-week deployment of CataractBot involving 318 patients and attendants who sent 1,992 messages, with 91.71% of responses verified by seven experts. Analysis of interaction logs revealed that medical questions significantly outnumbered logistical ones, hallucinations were negligible, and experts rated 84.52% of medical answers as accurate. As the knowledge base expanded with expert corrections, system performance improved by 19.02%, reducing expert workload. These insights guide the design of future LLM-powered chatbots.<|reference_end|>
@article{sachdeva2024learnings,
    title={Learnings from a Large-Scale Deployment of an LLM-Powered
  Expert-in-the-Loop Healthcare Chatbot},
    author={Bhuvan Sachdeva, Pragnya Ramjee, Geeta Fulari, Kaushik Murali, Mohit
  Jain},
    journal={arXiv preprint arXiv:2409.10354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10354},
    primaryClass={cs.HC}
}