Conformer-Based Dental AI Patient Clinical Diagnosis Simulation Using Korean Synthetic Data Generator for Multiple Standardized Patient Scenarios

• Published in: Bioengineering (Basel) Vol. 10; no. 5; p. 615
• Main Authors: Kim, Kangmin, Chun, Chanjun, Moon, Seong-Yong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.05.2023; MDPI
• Subjects: Artificial intelligence; Bioengineering; Clinical medicine; conformer; Customization; Data collection; Datasets; Deep learning; dental informatics; Education; Interviews; Machine translation; Medical personnel; Neural networks; Patients; Questions; Simulation; Skills; Speech; Synthetic data; Training; Transformers; Verbal communication; South Korea; clinical medicine; dental informatics; conformer; artificial intelligence
• DOI: 10.3390/bioengineering10050615
• ISSN: 2306-5354

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.