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Importance <p>Radiation dermatitis (RD) is a common complication of radiotherapy, affecting up to 90% of patients. AI-based automated assessment may improve objective grading and predictive accuracy.</p> Objective <p>To review AI applications in RD detection, grading, and severity prediction.</p> Evidence Review <p>This review summarizes the promising application of deep learning for automated RD grading and identifies a distinct lack of research into AI models for predicting its progression.</p> Findings <p>AI shows promise in reducing interobserver variability and enabling early intervention. Challenges include dataset limitations and lack of cross-center validation.</p> Conclusions <p>AI-based tools have potential for personalized RD management, but further multicenter validation is needed.</p> Clinical Trial registration <p>Clinical Trial registration: ChiCTR2400082684</p>

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Artificial intelligence-based automated grading and severity prediction of radiation dermatitis: a review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Zihan Shi,
  • Cong Zhang,
  • Yufen Liu,
  • Meixi Liu,
  • Ningning Li,
  • Yan Cui,
  • Hongfei Sun,
  • Lina Zhao

摘要

Importance

Radiation dermatitis (RD) is a common complication of radiotherapy, affecting up to 90% of patients. AI-based automated assessment may improve objective grading and predictive accuracy.

Objective

To review AI applications in RD detection, grading, and severity prediction.

Evidence Review

This review summarizes the promising application of deep learning for automated RD grading and identifies a distinct lack of research into AI models for predicting its progression.

Findings

AI shows promise in reducing interobserver variability and enabling early intervention. Challenges include dataset limitations and lack of cross-center validation.

Conclusions

AI-based tools have potential for personalized RD management, but further multicenter validation is needed.

Clinical Trial registration

Clinical Trial registration: ChiCTR2400082684