Prof. Kazumi Kubota of Research Organization as lead author was published in the International Nursing Review (Wiley) peer-reviewed international journal of Impact Factor (IF) 3.7. The published papers can be viewed from the following URL.

https://pubmed.ncbi.nlm.nih.gov/40970692

This paper theoretically criticizes the potential and challenges of integrating generative AI into nursing informatics based on the technology acceptance model (TAM), risk recognition frame, and the knowledge of the 2023 Japanese survey, and presents the direction of policy innovation, including education, ethics governance, and regulatory design. The analysis showed that while information access and quality of service are expected to improve, multidimensional strategies (education by target, ethical governance, and continuous feedback from the field) are essential for issues such as digital literacy disparity, responsibility, data accuracy, and privacy protection.

The results are of great significance as the first step in creating guidelines that cross practical, education, and policies for the safe and effective use of generative AI in nursing practice.

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