Prof. Kazumi Kubota of Faculty of Data Science as co-leader author was published in the peer-reviewed International Nursing Review (Wiley) of Impact Factor (IF) 3.7. The published papers can be viewed from the following URL.

https://pubmed.ncbi.nlm.nih.gov/40781830

This study explores the possibility of formalizing the “tacit knowledge” that midwives have cultivated over many years of experience using AI technology (machine learning and natural language processing) to utilize it in clinical settings and policy planning. The analysis showed that visualization of empirical knowledge helps prevent trauma and support clinical judgment, while maintaining ethical governance and privacy protection is essential.

The results are of great significance as a first step in creating a system that shares the potential knowledge of midwifery work throughout society and contributes to improving the quality of medical care, developing human resources, and designing systems.

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Shimonoseki City University
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