Generative Artificial Intelligence is set to revolutionize healthcare
delivery by transforming traditional patient care into a more personalized,
efficient, and proactive process. Chatbots, serving as interactive
conversational models, will probably drive this patient-centered transformation
in healthcare. Through the provision of various services, including diagnosis,
personalized lifestyle recommendations, and mental health support, the
objective is to substantially augment patient health outcomes, all the while
mitigating the workload burden on healthcare providers. The life-critical
nature of healthcare applications necessitates establishing a unified and
comprehensive set of evaluation metrics for conversational models. Existing
evaluation metrics proposed for various generic large language models (LLMs)
demonstrate a lack of comprehension regarding medical and health concepts and
their significance in promoting patients' well-being. Moreover, these metrics
neglect pivotal user-centered aspects, including trust-building, ethics,
personalization, empathy, user comprehension, and emotional support. The
purpose of this paper is to explore state-of-the-art LLM-based evaluation
metrics that are specifically applicable to the assessment of interactive
conversational models in healthcare. Subsequently, we present an comprehensive
set of evaluation metrics designed to thoroughly assess the performance of
healthcare chatbots from an end-user perspective. These metrics encompass an
evaluation of language processing abilities, impact on real-world clinical
tasks, and effectiveness in user-interactive conversations. Finally, we engage
in a discussion concerning the challenges associated with defining and
implementing these metrics, with particular emphasis on confounding factors
such as the target audience, evaluation methods, and prompt techniques involved
in the evaluation process.

人工智能的生成模型将通过将传统医疗转变为更加个性化、高效和主动的过程来革命性地改变医疗服务的交付，而聊天机器人作为互动对话模型将可能推动医疗的这种以患者为中心的转变。通过提供诊断、个性化的生活方式建议和心理健康支持等各种服务，旨在显著增强患者的健康结果，同时减轻医疗服务提供者的工作负担。本文的目的是探索适用于评估医疗互动对话模型的最新大型语言模型（LLM）评估指标，并提出一套全面的评估指标，旨在从最终用户的角度全面评估医疗聊天机器人的性能。这些指标包括语言处理能力的评估、对真实世界临床任务的影响以及对用户互动对话的有效性评估。最后，我们对定义和实施这些指标所面临的挑战进行讨论，特别强调评估过程中涉及的目标受众、评估方法和提示技术等混淆因素。