1. Introduction
Medical interviews are expected to undergo a major transformation through the use of artificial intelligence. However, artificial intelligence-based systems that support medical interviews are not yet widespread in Japan, and their usefulness is unclear. A randomized, controlled trial to determine the usefulness of a commercial medical interview support system using a question flow chart-type application based on a Bayesian model was conducted. Ten resident physicians were allocated to two groups with or without information from an artificial intelligence-based support system. The rate of correct diagnoses, amount of time to complete the interviews, and number of questions they asked were compared between the two groups. Two trials were conducted on different dates, with a total of 20 resident physicians participating. Data for 192 differential diagnoses were obtained. There was a significant difference in the rate of correct diagnosis between the two groups for two cases and for overall cases (0.561 vs. 0.393; “Listen to your patient; he is telling you the diagnosis”, taught William Osler, the great physician of the late 19th century. Peterson reported that taking a medical history alone led to a final diagnosis in 76% of cases [Recently, it has been suggested that information technology may have the potential to improve clinical problem-solving, such as by reducing morbidity and improving clinical decision-making [Unfortunately, AI-based medical interviewing systems have not yet been widely used due to the complexity of their operational methods and the difficulty associated with their maintenance and version upgrades. However, a few medical interview systems have recently been launched in Japan. In this pilot study, a newly launched artificial intelligence-based medical interviewing system with a proprietary algorithm using a Bayesian model was evaluated.We hypothesized that presenting results from a model constructed by an advanced machine learning algorithm based on appropriate data could improve the accuracy of differential diagnosis based on interviews and shorten the time required for those interviews. In our view, artificial intelligence-based decision support in the medical field will lead to standardization and improvement of the quality of medical care without having to rely on the experience of physicians. Experienced physicians are already more accurate with regard to diagnosis, with less variation compared to less-experienced ones such as resident physicians. Therefore, this study was designed based on the hypothesis that artificial intelligence provides particularly beneficial assistance to such less-experienced physicians, leading to our selection of resident physicians for the study as a sample.To clarify the above hypothesis, medical interviews were conducted as a preclinical experiment with simulated patients and first-year resident physicians to evaluate the effectiveness of a practical artificial intelligence-based medical interview-assistance system using a proprietary algorithm based on a Bayesian model.