Development of medical knowledge content for problem-solving competencies through dialogue with the undergraduate medical education community in Japan.

Background: Discrepancies existed between the medical knowledge sections of the Model Core Curriculum for Medical Education (MCC) and the Guidelines for the National Examination for Medical Practitioners (GNEMP) in Japan. These discrepancies have been one of the underlying factors hindering the development of learner-centered medical education in the country. The project team responsible for the 'Problem-Solving' section of the MCC aimed to address discrepancies between the disease lists in the MCC and the GNEMP.

Can AI-Generated Clinical Vignettes in Japanese Be Used Medically and Linguistically?

Background: Creating clinical vignettes requires considerable effort. Recent developments in generative artificial intelligence (AI) for natural language processing have been remarkable and may allow for the easy and immediate creation of diverse clinical vignettes.

Objective: In this study, we evaluated the medical accuracy and grammatical correctness of AI-generated clinical vignettes in Japanese and verified their usefulness.

A Cross-Sectional Study on Whether Comprehensively Gathering Information From Medical Records Is Useful for the Collection of Operational Characteristics.

This study tests whether comprehensively gathering information from medical records is useful for developing clinical decision support systems using Bayes' theorem. Using a single-center cross-sectional study, we retrospectively extracted medical records of 270 patients aged ≥16 years who visited the emergency room at the Tokyo Metropolitan Tama Medical Center with a chief complaint of experiencing headaches. The medical records of cases were analyzed in this study.

Expert assessment of ChatGPT's ability to generate illness scripts: an evaluative study.

Background: An illness script is a specific script format geared to represent patient-oriented clinical knowledge organized around enabling conditions, faults (i.e., pathophysiological process), and consequences.

Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: Comparison Study.

Background: Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably.