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This is a Bibliometric Analysis study.

October 5, 2025Health Sciences QuarterlyOpen Access

The explainable AI (XAI) in healthcare: A bibliometric analysis using VOSviewer and R Studio

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This bibliometric analysis reveals trends and key themes in explainable AI research in healthcare, highlighting collaboration across countries.

Exponential growth in explainable AI research in healthcare is expected to peak in 2024, emphasizing its increasing importance.
Key themes in the field include explainable AI, machine learning, and deep learning, indicating foundational concepts driving research.
Research identified prominent authors and collaborating countries, particularly the USA and China, shaping the landscape of XAI in healthcare.
Emerging themes like fairness, transparency, and trust highlight critical areas needing attention in future explainable AI research.

Şahin et al. (2025) studied this question.

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October 5, 2025Health Sciences QuarterlyOpen Access

View Full Paper

Ask AI

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Authors

This bibliometric analysis reveals trends and key themes in explainable AI research in healthcare, highlighting collaboration across countries.

Exponential growth in explainable AI research in healthcare is expected to peak in 2024, emphasizing its increasing importance.
Key themes in the field include explainable AI, machine learning, and deep learning, indicating foundational concepts driving research.
Research identified prominent authors and collaborating countries, particularly the USA and China, shaping the landscape of XAI in healthcare.
Emerging themes like fairness, transparency, and trust highlight critical areas needing attention in future explainable AI research.

Şahin et al. (2025) studied this question.

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