飯塚 玄明

Anti‐oppressive practice (AOP) provides a framework that challenges structural inequities and illuminates the lives of both patients and professionals. We introduce AOP into primary care in Japan.

OBJECTIVE: This study aimed to evaluate the association between the Yokohama Walking Point Program, which promotes walking through feedback on step counts and incentives, and the extension of healthy life expectancy. METHODS: A total of 4298 individuals aged over 65 years who responded to the 2013 and 2016 surveys and who were not certified as needing long-term care in 2016 were included in this study. The participants were categorized into "non-participation," "participation without uploading," and "participation with uploading" groups based on their involvement and uploading of pedometer data. The objective variable was the occurrence of long-term care certification and deaths over the subsequent four years. A modified Poisson regression model was applied, adjusting for 15 variables before project initiation. RESULTS: A total of 440 participants (10.2 %) were included in the "participation with uploading" group and 206 (4.8 %) in the "participation without uploading" group. Compared with "non-participation," the risk ratio was 0.77 (95 % confidence interval (CI): 0.59-0.99) for "participation with uploading" and 1.02 (95 % CI: 0.75-1.38) for "participation without uploading". In the sensitivity analysis censoring death as an inapplicable outcome and considering functional decline, participation with uploading showed a risk ratio of 0.79 (95 % CI: 0.60-1.04) for the likelihood of functional decline. CONCLUSIONS: The use of pedometers and health point programs based on walking activity is associated with enhancing the health of older individuals participating in the program, representing a population-centric strategy targeting all citizens.

BACKGROUND: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. OBJECTIVE: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. METHODS: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). RESULTS: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). CONCLUSIONS: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.