Kim Kangmin, Chun Chanjun, Moon Seong-Yong
Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.
Department of Oral and Maxillofacial Surgery, College of Dentistry, Chosun University, Gwangju 61452, Republic of Korea.
Bioengineering (Basel). 2023 May 19;10(5):615. doi: 10.3390/bioengineering10050615.
The goal of clinical practice education is to develop the ability to apply theoretical knowledge in a clinical setting and to foster growth as a professional healthcare provider. One effective method of achieving this is through the utilization of Standardized Patients (SP) in education, which familiarizes students with real patient interviews and allows educators to assess their clinical performance skills. However, SP education faces challenges such as the cost of hiring actors and the shortage of professional educators to train them. In this paper, we aim to alleviate these issues by utilizing deep learning models to replace the actors. We employ the Conformer model for the implementation of the AI patient, and we develop a Korean SP scenario data generator to collect data for training responses to diagnostic questions. Our Korean SP scenario data generator is devised to generate SP scenarios based on the provided patient information, using pre-prepared questions and answers. In the AI patient training process, two types of data are employed: common data and personalized data. The common data are employed to develop natural general conversation skills, while personalized data, from the SP scenario, are utilized to learn specific clinical information relevant to a patient's role. Based on these data, to evaluate the learning efficiency of the Conformer structure, a comparison was conducted with the Transformer using the BLEU score and WER as evaluation metrics. Experimental results showed that the Conformer-based model demonstrated a 3.92% and 6.74% improvement in BLEU and WER performance compared to the Transformer-based model, respectively. The dental AI patient for SP simulation presented in this paper has the potential to be applied to other medical and nursing fields, provided that additional data collection processes are conducted.
临床实践教育的目标是培养在临床环境中应用理论知识的能力，并促进成为专业医疗服务提供者的成长。实现这一目标的一种有效方法是在教育中利用标准化病人（SP），这使学生熟悉真实的患者访谈，并使教育工作者能够评估他们的临床技能。然而，SP教育面临着诸如雇佣演员的成本以及培训他们的专业教育工作者短缺等挑战。在本文中，我们旨在通过利用深度学习模型来取代演员来缓解这些问题。我们采用Conformer模型来实现人工智能患者，并开发了一个韩国标准化病人情景数据生成器，以收集用于训练对诊断问题的回答的数据。我们的韩国标准化病人情景数据生成器旨在根据提供的患者信息，使用预先准备好的问题和答案来生成标准化病人情景。在人工智能患者训练过程中，使用了两种类型的数据：通用数据和个性化数据。通用数据用于培养自然的一般对话技能，而来自标准化病人情景的个性化数据则用于学习与患者角色相关的特定临床信息。基于这些数据，为了评估Conformer结构的学习效率，使用BLEU分数和WER作为评估指标与Transformer进行了比较。实验结果表明，基于Conformer的模型在BLEU和WER性能方面分别比基于Transformer的模型提高了3.92%和6.74%。本文提出的用于标准化病人模拟的牙科人工智能患者有潜力应用于其他医学和护理领域，前提是进行额外的数据收集过程。
