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REVIEW ARTICLE

Artificial intelligence in radiation dose optimization research: A bibliometric mapping

Norhayati Mohd Zain1†* ,  Wan Azani Mustafa2†* ,  Nur Anis Izzati Che Mut1 ,  Muhammad Syamsul Mohamed Zuki1
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1 Department of Medical Imaging, School of Integrative Medicine and Life Sciences, KPJ Healthcare University, Nilai, Negeri Sembilan , Malaysia
2 Department of Mechatronic, Faculty of Electrical Engineering Technology, Campus Pauh Putra, Universiti Malaysia Perlis, Arau, Perlis , Malaysia
†These authors contributed equally to this work.
AIH, 10.36922/AIH026250064 https://doi.org/10.36922/AIH026250064
Received: 18 June 2026 | Revised: 14 August 2026 | Accepted: 17 August 2026 | Published online: 17 September 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

In radiation dose optimization, artificial intelligence (AI) plays a crucial role in improving image quality, treatment planning, and patient safety in medical imaging and radiotherapy. Although research in this field is growing rapidly, publication patterns, intellectual structure, themes, and co-authorship remain poorly understood. This study therefore aimed to systematically visualize the global AI research landscape in radiation dose optimization through a bibliometric approach. Data were retrieved from the Scopus database, obtained through a structured search strategy, yielding a final dataset of 738 publications. We performed bibliometric analysis with BiblioSpy®, data cleaning with OpenRefine, and network visualization and keyword co-occurrence analysis with VOSviewer. The results showed a substantial increase in publication output since 2018, reaching its highest annual output in 2025 with 187 publications; data for 2026 reflect an incomplete year, reflecting growing scholarly attention to AI-driven radiation dose optimization in medical imaging and radiotherapy. The United States and China led in research productivity and international collaboration. Medical Physics and IEEE Transactions on Medical Imaging showed high citation performance and were the two publications with the strongest scholarly influence. The keyword analysis revealed the main research topics: computed tomography, deep learning, machine learning, image reconstruction, and dose prediction. Co-authorship analysis also showed a strong trend toward large-scale, international collaboration. In conclusion, this research provides a thorough bibliometric analysis of AI research in radiation dose optimization, highlighting key trends, emerging areas, and international research collaborations to guide future studies and clinical practice.

Graphical abstract
Keywords
Bibliometrics
VOSviewer
Artificial intelligence
Dose optimization
Funding
This research received internal financial support from the Research Management Department, KPJ Healthcare University, Nilai, Negeri Sembilan, Malaysia.
Conflict of interest
The authors report no potential conflict of interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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