Trends in diagnostic error research across Asia: a quantitative content analysis

Abstract

OBJECTIVES: This study examined recent trends in diagnostic error research across Asia-Pacific region, with a focus on Japan, using quantitative content analysis. METHODS: A PubMed search identified diagnostic error-related publications from Asian countries, Australia, and New Zealand between January 2016 and July 2025. Three datasets (Asia-Pacific, Japan, Asia-Pacific excluding Japan) were created. Article titles were analyzed using KH Coder to generate co-occurrence networks and identify key research themes. Temporal trends were assessed using correspondence analysis with publication year as an external variable. RESULTS: A total of 815 articles were retrieved. Over 90 % originated from five high-gross domestic product (GDP) countries (China, Japan, Australia, India, and South Korea). Shared themes included diagnostic error, clinical characteristics, AI and machine learning, and study type. Japan was characterized by studies from general internal medicine and primary care, including malpractice claims and trainee education, whereas other regions emphasized cancer diagnostics, molecular and translational medicine, and AI system development. From 2023 onward, AI-related terms became increasingly prominent. CONCLUSIONS: Diagnostic error research in the Asia-Pacific is highly concentrated and reflects differing healthcare contexts and resource disparities. Strengthening regional networks, joint funding mechanisms, and collaboration with initiatives may enhance diagnostic safety and equity across the region.

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