ArticleInternational journal of particle therapy2026
Artificial Intelligence in Particle Therapy Treatment Planning: Current Advances and Future Directions.
Article in International journal of particle therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
2 authors.
Funding
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Abstract
Purpose: This review examines the role of artificial intelligence (AI) in particle therapy treatment planning, highlighting recent advancements, clinical potential, and existing challenges. Methods and Materials: A review was conducted on AI applications in proton and heavy ion therapy, focusing on automated plan generation, dose prediction, treatment adaptation, and quality assurance. Results: AI-driven methods have shown promise in optimizing treatment planning, enhancing dose prediction, and improving plan adaptation. However, challenges such as data limitations, model interpretability, and regulatory barriers hinder clinical implementation. Conclusion: While AI offers significant potential to improve particle therapy treatment planning, further research is needed to enhance model robustness, clinical validation, and integration into existing workflows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.